COLOUR REMOVAL FROM SUGAR CANE JUICE - QUT · phenolic acids present in sugar cane juice. The study was able to identify flavonoids ... into your office with the heater running on

COLOUR REMOVAL FROM

SUGAR CANE JUICE

Danny M. T. Nguyen

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

School of Chemistry, Physics and Mechanical Engineering

Science and Engineering Faculty

Queensland University of Technology, Brisbane, Australia

June 2013

ii

Supervisor: Professor William O. S. Doherty

Sugar Research and Innovation

Centre for Tropical Crops and Biocommodities

Queensland University of Technology, Brisbane,

Australia

Associate Supervisor: Adjunct Associate Professor John P. Bartley

School of Chemistry, Physics and Mechanical

Engineering

Science and Engineering Faculty

Queensland University of Technology, Brisbane,

Australia

The research was carried out within the Centre for Tropical Crops and Biocommodities at the

Queensland University of Technology.

iii

IMPORTANT NOTICE

The information in this thesis is confidential and should not be disclosed for any

reason or relied on for a particular use or application. Any invention or other

intellectual property described in this document remains the property of the

Queensland University of Technology.

iv

DECLARATION OF AUTHORSHIP

The work contained in this thesis has not been submitted for assessment for any other

award. Wherever contributions of others are involved, every effort is made to

indicate this clearly with proper reference to the literature and acknowledgement of

collaborative research and discussions. Some parts of the research work in this thesis

have been published and a list of publications arising from this research has been

provided.

.. ...

Danny M. T. Nguyen, BSc (Hons)

Date: ..........................................................

QUT Verified Signature

v

Abstract

One of the most important parameters in raw sugar quality is colour.

Australian raw sugars are considered to be of high quality with respect to this

parameter. However, some raw sugars produced in both Australia and overseas are

relatively difficult to decolourise by sugar refiners, and tend to develop colour during

storage. A new approach that has the potential to efficiently and cost-effectively

decolourise sugar process streams is through the use of the Fenton oxidation and

related processes. The Fenton oxidation process involves the catalytic production of

hydroxyl radicals from the decomposition of hydrogen peroxide (H2O2) using iron(II),

which has the potential to effectively degrade colour and colour precursors present in

aqueous systems.

As a first step towards developing this technology, this study determined the

colour content and the composition of colour precursors (i.e., phenolic acids), present

in sugar cane juices processed by Australian sugar factories. The results showed that

caffeic, p–coumaric and ferulic acids (classed as hydroxycinnamic acids) are the main

phenolic acids present in sugar cane juice. The study was able to identify flavonoids

(e.g., chrysin, morin, quercetin and rutin) because of modifications of the methods

used in the evaluation of colourants in sugar cane juice.

The results also show that juice expressed partly or solely from whole crop

harvested cane, has significantly higher colour (11,400–20,000 IU) than juices

expressed from burnt harvested cane (10,400–12,700 IU). However, the

concentrations of phenolic acids in burnt cane were twice as much as those obtained

in whole crop cane. This is probably due to the thermal decomposition of HMW

phenolics (viz., lignin, polyphenols) during cane burning.

The Fenton oxidation process was used to study the degradation of these

hydroxycinnamic acids (i.e., caffeic, p–coumaric and ferulic) in water and sucrose

solutions. Central composite design and response surface methodologies were used to

evaluate and optimise the interactive effects of the process parameters. Quadratic

polynomial models were developed for the degradation of each of the individual

vi

acids, and the total hydroxycinnamic acid mixtures. The optimum degradation

efficiency (77%) in an aqueous solution containing the hydroxycinnamic acids

(200 mg/L) was optimum at pH 4.7 and at 25 °C. The efficiency dropped in the

presence of sucrose to 57% at pH 5.4 and at 36 °C.

In a mixture of these hydroxycinnamic acids, the degradation behaviour of

caffeic acid differed from those of p–coumaric and ferulic acids, because unlike the

other acids, it forms a complex with iron(III). Iron(III) is produced in situ during the

oxidation process.

Analysis of the Fenton degradation products showed the presence of low

molecular weight phenolics, aliphatic carboxylic acids as well as several oligomer

products. The tentative mechanisms of formation of these compounds have been

proposed.

To improve the effectiveness of the Fenton process, aluminium chloride was

added to act as a pro-oxidant. This process was evaluated on a synthetic juice

solution consisting of sucrose (15% (w/w)), the hydroxycinnamic acids (200 mg/L)

and a synthetic glucose-glycine melanoidin (2,000 mg/L). The modified Fenton

process degraded the melanoidin and the hydroxycinnamic acid mixture by

approximately 69% and 53% respectively. In the absence of aluminium chloride, the

Fenton process on its own resulted in 63% and 47% degradation, respectively but only

achieved 24% decolourisation. However, the addition of aluminium chloride played a

significant role in the removal of colour with up to 43% decolourisation achieved.

The modified Fenton process was then evaluated for the decolourisation of

authentic factory juices. There were increases in colour measured at pH 4.0 (≤ 45%)

and pH 7.0 (≤ 21%). However, there was decrease for the colour measured at pH 9.0

(≤ 42%). Colour is usually measured at pH 7.0 but additional information about the

nature of colourants is obtained at pH 4.0 and pH 9.0. Colour measured at pH 4.0

suggests the presence the presence of high molecular weight colourants, while colour

measured at pH 9.0 is due to the presence of natural colourants such as flavonoids and

phenolics. The colour at pH 9.0 is more likely to be transferred to the crystal, so there

may well be colour reduction if the treated juice is further processed to raw sugar.

vii

The key contribution contained in this thesis is an understanding of the

degradation of colour precursors in sugar solutions. A new direction of research for

the removal of colour and colour precursors in sugar process streams has been

identified.

viii

Keywords

Colour

Colourants

Colour precursors

Sugar

Sugar cane juice

Sugar quality

Sucrose

Decolourisation

Degradation

Fenton

Advanced oxidation process

Hydroxycinnamic acids

Caffeic acid

p–Coumaric acid

Ferulic acid

Response surface methodology

Experimental design

UV/Visible spectroscopy

High-performance liquid chromatography

Reaction pathways

Clarification

Aluminium chloride

Melanoidin

Reducing sugars

ix

Acknowledgements

A PhD candidature, by its very own nature, is a very unique endeavour. There

is no right way to undertake a PhD project. However, there are many wrong ways

that one could take throughout their candidature. I am for one, a very glad person,

who has taken the best path possible in order to complete my candidature and

hopefully graduate with a doctoral degree. I could have not taken this path without

the consistent guidance and advice given from the very kind people that I have met

throughout my entire candidature to whom I give thanks to.

First and foremost, I would like to sincerely thank my primary supervisor,

Prof. William (Bill) Doherty, for his constant patience, guidance, encouragement and

commitment to this work. Bill, you have been a great mentor. I have learnt and

gained so much from you. Despite our differences and heated discussions on several

aspects of this thesis, you have always seen the best in me. Towards the end of

writing this thesis, I was asked by many for an inspirational and memorable quote

from you. In response to that, that would definitely be, “Danny, could you please

come to my office? I need to see you.” I am very glad because every time I walked

into your office with the heater running on a warm Brisbane day, I would learn

something new, no matter how irrelevant it is to my own work. Thank you.

I would also like to thank my associate supervisor, Adj. A/Prof. John Bartley.

You have always been prompt whenever I needed you most and you have been a great

mentor. I appreciate all the times, especially at the very early stages of my

candidature, assisting me with certain aspects of organic chemistry. I always gained

something useful each time we met. Up to today, I still have a strong passion for

organic chemistry and to me, drawing chemical structures for reaction mechanisms is

genuinely a form of art.

This project would have not happened, if it was not for the financial support

from my scholarship sponsors. To the three main sponsors, the Queensland

University of Technology (QUT), the Sugar Research and Development Corporation

and Sugar Research Limited, a very big thank you for your generosity. My exposure

x

to the Australian sugar industry has been very worthwhile. This was a very rare

opportunity and I am grateful that each sponsor accepted me to undertake this project.

In addition, I would also like to thank the production staff of Condong Sugar Mill,

Tully Sugar Mill and Isis Central Sugar Mill who gave me access to their facilities

during the crushing seasons.

Many thanks must go to all the academic and technical staff who have

contributed to this project throughout my candidature. Prof. Robert (Bob) Gilbert

(University of Queensland, UQ) for his expertise on food polymers; Dr. Peter Sopade

(UQ) and A/Prof. Geoff Kent (QUT) for introducing me into the world of multivariate

statistics; Mr. Hakan Bakir (QUT) for his assistance at the mills during the factory

trials; Mr. Tony Raftery (QUT) for his assistance on XRD analysis; Dr. Chris

Carvalho and Mrs. Leonora Newby for analytical instrument training; and Ms. Wanda

Stolz (QUT) for her endless hospitality in the lab.

To my fellow colleagues who work closely with me, thank you for your

ongoing support. Chris East, you have been a great mate throughout my candidature

and thanks for changing my life that day (you know what happened). William

Gilfillan, thanks for keeping a lookout for Bill every time he approaches into our

office. Travelling with you to the conferences has been great. You always take the

best photos! Josh Howard, thanks for your feedback during our research meetings.

All the best with your PhD mate. Darryn Rackemann, massive thanks for your

hospitality in general as well as your advice on various aspects of the sugar industry.

It has been great to work alongside you. Caroline Thai for her patience and

generosity throughout our university lives since the days back at RMIT University. I

am sorry, if I ever convinced you to do a PhD but in the end we know it was worth it.

Thank you for the seven years of memories. A final message to the whole group, I

am very glad to have met all of you and I wish you all the best throughout your

careers.

There are far too many people to list all of them individually, but I am

indebted to all of them at one time or another, for their support and giving me the

motivation to complete my candidature. These people are all the staff and students

from the Centre of Tropical Crops and Biocommodities (QUT), the School of

Chemistry, Physics and Mechanical Engineering (QUT) and the administrative and

xi

HDR support staff (QUT). Also, to the SEF HDR Student Society at QUT, thank you

for giving me the opportunity to be the founding chairman of the society. It has been

a pleasure during the inaugural year and I wish all the best for the team in the future.

There is one more group of people that I am very fond and close to that I need

to recognise for their long-distance support and love, that is my family. Leaving

home and family for a long period of time (once again… sorry mum!) was not so

easy. I cannot remember how long since I left home but the words “Không có văn

bằng học là big trouble! Okay?” (Vietnamese: No (PhD) degree means big trouble

(for you)! Okay?) are still ringing in my ear. Maybe that has been a driving force for

me to finish my candidature. To my family back in Melbourne, thank you and I will

always make the both of you, mum and dad, proud! To my brother, Steven, good luck

with Year 12 exams. Considering taking chemistry in university next year? You

should!

xii

I dedicate this thesis to my family and friends for

nursing me with affections and love and for their

dedication for success in my life.

“The surest way not to fail is to determine to

succeed.”

Rt. Hon. Richard Brinsley Sheridan

xiii

Publications and Awards

Refereed Journal Papers

Nguyen, D. M. T., & Doherty, W. O. S. (2013). Optimisation of process parameters

for the removal of hydroxycinnamic acids in sugar solutions. International

Sugar Journal, accepted for publication.

Nguyen, D. M. T., & Doherty, W. O. S. (2012). Optimisation of process parameters

for the degradation of caffeic acid in sugar solutions. International Journal of

Food Science and Technology, 47(12), 2477-2486.

Nguyen, D. M. T., & Doherty, W. O. S. (2012). Phenolics in sugar cane juice:

Potential degradation by hydrogen peroxide and Fenton's reagent.

International Sugar Journal, 114(1361), 309-315.

Conference Proceedings

Nguyen, D. M. T., & Doherty, W. O. S. (2012) Process optimisation for the

degradation of phenolic compounds in water and sugar solutions. Proceedings

of the Second International Conference on Advanced Oxidation Processes, 72-

73.

Nguyen, D. M. T., & Doherty, W. O. S. (2012) Optimisation of process parameters

for the removal of hydroxycinnamic acids in sugar solutions. Proceedings of

the Australian Society of Sugar Cane Technologists, 34, (electronic format).

Nguyen, D. M. T., & Doherty, W. O. S. (2011) Phenolics in sugar cane juice:

Potential degradation by hydrogen peroxide and Fenton’s reagent.

Proceedings of the Australian Society of Sugar Cane Technologists, 33,

(electronic format).

xiv

Conference Posters

Nguyen, D. M. T., & Doherty, W. O. S. (2012) Combined Fenton oxidation and

chemical coagulation for the treatment of melanoidin/phenolic acid mixtures.

Presented at the Second International Conference on Advanced Oxidation

Processes, Kottayam, Kerala, India.

Conference Lectures

Nguyen, D. M. T., & Doherty, W. O. S. (2012) Process optimisation for the

degradation of phenolic compounds in water and sugar solutions. Presented at

the Second International Conference on Advanced Oxidation Processes,

Kottayam, Kerala, India.

Nguyen, D. M. T., & Doherty, W. O. S. (2012) Optimisation of process parameters

for the removal of hydroxycinnamic acids in sugar solutions. Presented at the

34th Australian Society of Sugar Cane Technologists, Palm Cove,

Queensland, Australia.

Nguyen, D. M. T., & Doherty, W. O. S. (2011) Phenolics in sugar cane juice:

Potential degradation by hydrogen peroxide and Fenton’s reagent. Presented

at the 33rd Australian Society of Sugar Cane Technologists, Mackay,


Awards

Presenting Science Award (2013) for the best presentation presented at the Sugar

Research and Development Corporation Scholarship Forum, Townsville,


Young Investigators Award (2012) for the best paper presented at the Second

International Conference on Advanced Oxidation Processes, Kottayam,

Kerala, India.

Denis Foster Chemistry/Chemical Engineering Award (2012) for the best paper

presented by a chemistry/chemical engineering tertiary student at the 34th

Australian Society of Sugar Cane Technologists Conference, Palm Cove,


xv

Table of Contents

Abstract............................................................................................................................ v

Keywords......................................................................................................................... viii

Acknowledgements......................................................................................................... ix

Publications and Awards................................................................................................. xiii

List of Figures.................................................................................................................. xx

List of Tables................................................................................................................... xxvii

List of Abbreviations and Nomenclature........................................................................ xxxi

1. GENERAL INTRODUCTION........................................................................... 1

1.1 Background and Motivation.................................................................... 2

1.2 Research Problem..................................................................................... 3

1.3 Aims and Objectives................................................................................. 4

1.4 Scope of this Thesis................................................................................... 5

2. LITERATURE REVIEW.................................................................................... 9

2.1 Introduction............................................................................................... 10

2.2 Colourants in Sugar Process Streams..................................................... 10

2.2.1 Naturally Occurring Colourants.................................................. 12

2.2.2 Factory Produced Colourants...................................................... 16

2.3 Reactivity of Colourants during Sugar Manufacturing........................ 18

2.3.1 Enzymatic Browning..................................................................... 18

2.3.2 Non-enzymatic Oxidation............................................................. 20

2.3.3 Maillard Reaction......................................................................... 21

2.3.4 Caramelisation............................................................................. 24

2.3.5 Hexose Alkaline Degradation....................................................... 27

2.3.6 Conversion of Anthocyanins to Chalcones................................... 29

2.3.7 Biochemical Precursors of Flavonoids........................................ 30

2.4 Colour in Sugar Process Streams............................................................ 31

2.4.1 Effects of Temperature on Colour Formation.............................. 35

2.5 Sugar Decolourisation Technologies....................................................... 37

2.5.1 Current Technologies................................................................... 37

xvi

2.5.2 Decolourisation using Chemical Additives.................................. 38

2.5.3 Novel and Potential Technologies................................................ 42

3. DETERMINATION OF PHENOLIC COMPOUNDS IN FACTORY

SUGAR CANE JUICES.......................................................................................

57

3.1 Introduction................................................................................................. 58

3.2 Materials and Methods............................................................................... 58

3.2.1 Reagents and Solvents.................................................................. 58

3.2.2 Specification of Samples............................................................... 59

3.2.3 Sample Preparation...................................................................... 60

3.2.4 Instrumental Procedures and Analyses........................................ 60

3.2.5 Colour, Refractive Index and Total Soluble Solids

Measurements...............................................................................

62

3.3 Results and Discussion................................................................................ 62

3.3.1 Colour Analyses of Juices............................................................. 62

3.3.2 Phenolic Content in Juices........................................................... 63

3.4 Summary...................................................................................................... 70

4. DEGRADATION OF HYDROXYCINNAMIC ACIDS................................... 73

4.1 Introduction................................................................................................. 74



4.2.2 Catalytic and Non-catalytic Oxidation of Caffeic Acid................ 75

4.2.3 Fenton Oxidation Reactions for Caffeic Acid Degradation......... 76

4.2.4 Fenton Oxidation Reactions for the Degradation of

Hydroxycinnamic Acid Mixtures..................................................

78


4.2.6 Performance Assessment of the Fenton Oxidation Process......... 79

4.2.7 Design of Experiments.................................................................. 80

4.2.8 Statistical Analysis........................................................................ 82

4.2.9 Evaluation of the Interactions between Fe(II) and

Hydroxycinnamic Acids................................................................

82


4.3.1 Catalytic and Non-catalytic Oxidation of Caffeic Acid in

Aqueous Systems...........................................................................

83

xvii

4.3.2 Optimisation of Process Parameters for the Degradation of

Caffeic Acid in Sugar Solutions....................................................

87

4.3.3 Degradation of Hydroxycinnamic Acid Mixtures......................... 100

4.4 Summary...................................................................................................... 130

5. SEPARATION AND IDENTIFICATION OF FENTON OXIDATION

PRODUCTS DERIVED FROM HYDROXYCINNAMIC ACIDS.................

137

5.1 Introduction................................................................................................. 138




Hydroxycinnamic Acid Mixtures..................................................

138

5.2.3 Sample Preparation...................................................................... 139


5.2.5 Fenton Oxidation Reactions for the Degradation of Sucrose

Mixtures........................................................................................

142

5.2.6 Computational Methods............................................................... 142


5.3.1 Identification of Oxidation Products............................................ 143

5.3.2 Proposed Degradation Pathways of Selected Hydroxycinnamic

Acids.............................................................................................

153

5.3.3 Oligomerisation of Hydroxycinnamic Acids................................. 166

5.4 Summary...................................................................................................... 171

6. DEGRADATION OF MELANOIDIN AND HYDROXYCINNAMIC

ACID MIXTURES...............................................................................................

177

6.1 Introduction................................................................................................. 178



6.2.2 Preparation of Synthetic Melanoidin........................................... 179

6.2.3 Modified Fenton Oxidation Process............................................. 179


6.2.5 Performance Assessment of the Modified Fenton Oxidation

Process..........................................................................................

180

6.2.6 Design of Experiments.................................................................. 181

xviii

6.2.7 Statistical Analysis........................................................................ 182


6.3.1 Monitoring Melanoidin and Hydroxycinnamic Acid

Degradation..................................................................................

182

6.3.2 Transformation of Data, Regression Modelling and Statistical

Analysis.........................................................................................

184

6.3.3 Oxidation Performance of Melanoidins....................................... 190

6.3.4 Oxidation Performance of Hydroxycinnamic Acids..................... 194

6.3.5 Response Surface Analyses for the Decolourisation of Mixtures. 198

6.3.6 Process Optimisation and Validation........................................... 200

6.4 Summary...................................................................................................... 203

7. EVALUATION OF FENTON AND FENTON-LIKE PROCESSES FOR

THE REMOVAL OF COLOUR FROM FACTORY SUGAR CANE

JUICE....................................................................................................................

205

7.1 Introduction................................................................................................. 206



7.2.2 Specification of Samples............................................................... 207

7.2.3 Decolourisation Procedure.......................................................... 207

7.2.4 Preparation of Flocculants........................................................... 207

7.2.5 Preparation of Lime Saccharate................................................... 208

7.2.6 Clarification Procedure................................................................ 208

7.2.7 Turbidity Measurements............................................................... 209

7.2.8 Sucrose, Dry Substance and Purity Measurements...................... 210

7.2.9 Reducing Sugars Composition Analyses...................................... 210


Measurements...............................................................................

210

7.2.11 Inorganic Ion Composition Analyses........................................... 211


7.3.1 First Decolourisation Trials......................................................... 211

7.3.2 Second Decolourisation Trials..................................................... 215

7.3.3 Economic Considerations............................................................. 222

7.4 Summary...................................................................................................... 222

xix

8. CONCLUSIONS AND FUTURE ASPECTS..................................................... 227

8.1 Findings of the Thesis................................................................................. 228

8.2 Recommendations for Future Work......................................................... 231

Appendices............................................................................................................ 235

xx

List of Figures

Figure 2.1 Schematic flowchart of the sugar manufacturing process in

Australia…………………………………………………………

11

Figure 2.2 Flavonoid structures found in sugar process streams and

products. Examples are given under the general chemical

structures (Ververidis et al., 2007)...............................................

13

Figure 2.3 Structures of phenolics found in sugar process streams and


structures (Harborne, 1989; Vermerris and Nicholson, 2006).....

14

Figure 2.4 Polymerisation of monomeric gallic acid to polyphenols ellagic

acid and ellagitannin (Ross et al., 2007)......................................

15

Figure 2.5 Redox chemistry of phenolics under copper and iron to produce

colour forming products as proposed by Danilewicz et al.

(2008)............................................................................................

20

Figure 2.6 An example of a basic melanoidin structure formed from

3–deoxyhexosuloses (Cämmerer et al., 2002)..............................

26

Figure 2.7 An example of a melanoidin polymer formed from

3–deoxyhexosuloses and amino acids proposed by Cämmerer

and Kroh (1995)............................................................................

27

Figure 2.8 Condensation product formed from the reaction of HMF and a

ketone; followed by an additional condensation reaction with a

second equivalent of HMF (Chheda and Dumesic, 2007)…........

27

Figure 2.9

Formation of colour among three clarification processes; mixed

juice (MJ), heated juice (HJ), incubated juice (IJ), limed juice

(LJ), flocculated heated limed juice (FHLJ), evaporator supply

juice (ESJ), final evaporator syrup (FES) and raw sugar (RS)

(Eggleston et al., 2003).................................................................

34

xxi

Figure 3.1 Separation of a typical mixture of compounds in the FEJ extract

of burnt harvested cane by HPLC-DAD (Method A, UV/Vis

detection at 280 nm). 1 = gallic acid (tentative), 2 = HMF,

3 = 4–hydroxybenzoic acid, 4 = chlorogenic acid, 5 = vanillic

acid, 6 = caffeic acid, 7 = 2,3–dihydroxybenozic acid,

8 = protocatechuic acid (tentative), 9 = p–coumaric acid,

10 = ferulic acid............................................................................

64

Figure 3.2 Separation of a standard mixture of compounds by HPLC-DAD

(Method B, UV/Vis detection at 280 nm). 1 = gallic acid,

2 = HMF, 3 = protocatechuic acid, 4 = furfural,

5 = 4–hydroxybenzoic acid, 6 = (±)–catechin, 7 = vanillic acid,

8 = caffeic acid, 9 = chlorogenic acid, 10 = vanillin,

11 = p–coumaric acid, 12 = syringaldehyde, 13 = ferulic acid,

14 = sinapinic acid, 15 = coumarin, 16 = o–coumaric acid,

17 = 3,4,5–trimethoxybenzoic acid, 18 = rutin, 19 = diosmin,

20 = chrysin, 21 = morin, 22 = quercetin.....................................

67

Figure 3.3 Separation of a typical mixture of compounds in the PJ extract

of burnt harvested cane by HPLC-DAD (Method B, UV/Vis

detection at 280 nm). 1 = gallic acid, 2 = HMF,

3 = protocatechuic acid, 4 = furfural, 5 = 4–hydroxybenzoic

acid, 6 = vanillic acid, 7 = caffeic acid, 8 = p–coumaric acid,

9 = syringaldehyde, 10 = ferulic acid, 11 = sinapinic acid,

12 = coumarin, 13 = rutin, 14 = diosmin, 15 = chrysin,

16 = morin, 17 = quercetin...........................................................

68

Figure 4.1 Schematic representation of heating block used for the Fenton

oxidation process..........................................................................

77

Figure 4.2 Absorption spectra of CaA after the addition of 2.94 mM H2O2

at pH 3.0 at 25 °C.........................................................................

84

Figure 4.3

Degradation of CaA (measured at 320 nm) using Fenton’s

reagent at different initial pH at 25 °C. Concentrations of

H2O2: (a) 11.8 mM and (b) 2.94 mM...........................................

86

Figure 4.4 Plot of predicted and experimental (actual) values for the

degradation (%) of CaA................................................................

91

xxii

Figure 4.5 Normal probability plot of residuals for fitted model using CaA

degradation data............................................................................

91

Figure 4.6 Three-dimensional surface plots of CaA degradation (%) as a

function of (a) CaA and Fe(II); (b) sucrose and H2O2; (c)

sucrose and temperature; and (d) pH and Fe(II). Variables:

CaA (1.11 mM); sucrose (0% (w/w)); pH (5.0); Fe(II) (0.45

mM); H2O2 (6.62 mM); temperature (35 °C) and time (120 s)....

93


function of (a) pH and H2O2; (b) Fe(II) and H2O2; (c) H2O2 and

temperature; and (d) H2O2 and time. Variables: CaA

(1.11 mM); sucrose (0% (w/w)); pH (5.0); Fe(II) (0.45 mM);

H2O2 (6.62 mM); temperature (35 °C) and time (120 s)..............

95


degradation data before power transformation.............................

101

Figure 4.9 Box-Cox plots of (a) CaA and (b) pCoA degradation data for

the determination of the optimised power transformed response

surface models..............................................................................

103

Figure 4.10 Box-Cox plots of (a) FeA and (b) total HCA degradation data

for the determination of the optimised power transformed

response surface models...............................................................

104

Figure 4.11 Normal probability plots of residuals for fitted model using

(a) CaA and (b) pCoA degradation data after power

transformation...............................................................................

105


(a) FeA and (b) total HCA degradation data after power

transformation...............................................................................

106

Figure 4.13 Plots of predicted response and experimental (actual) values for

the degradation (%) of (a) CaA and (b) pCoA.............................

114

Figure 4.14

Plots of predicted response and experimental (actual) values for

the degradation (%) of (a) FeA and (b) total HCA.......................

115

xxiii

Figure 4.15 Perturbation plots for the degradation (%) of (a) CaA; (b) pCoA

and (c) FeA. Coded values are shown for each factor: total

HCA (A); sucrose (B); pH (C) and temperature (D); and refer

to actual values listed in Table 4.3...............................................

117

Figure 4.16 Effect of pH (pH 4.0–6.0) on the absorption spectra of CaA

(0.055 mM) at 25 °C: (a) in the absence and (b) in the presence

of Fe(II) (0.04 mM)......................................................................

119

Figure 4.17 Normalised ATR-FTIR spectra of CaA solutions at 25 °C after

subtraction of acetate buffer (pH 5.5): (a) in the absence and

(b) in the presence of Fe(II)..........................................................

122

Figure 4.18 Normalised ATR-FTIR spectra of CaA solutions containing

sucrose at 25 °C after subtraction of acetate buffer (pH 5.5): (a)

in the absence and (b) in the presence of Fe(II)...........................

123

Figure 4.19 Three-dimensional surface plots of total HCA degradation (%)

as a function of (a) total HCA and sucrose; (b) sucrose and pH;

and (c) pH and temperature. Variables: total HCA (155 mg/L);

sucrose (7.5% (w/w)); pH (5.0) and temperature (35 °C)............

124

Figure 5.1 High-performance LC-DAD chromatograms (UV/Vis detection

at 280 nm) of (a) CaA; (b) pCoA and (c) FeA; subjected to

Fenton oxidation at 2 min (pH 4.7, 25 °C)...................................

145

Figure 5.2 Total ion chromatograms (negative ion mode ESI-MS) of

(a) CaA; (b) pCoA and (c) FeA; subjected to Fenton oxidation

at 2 min (pH 4.7, 25 °C)...............................................................

148

Figure 5.3 Gas chromatograms of SPE extracts of (a) CaA; (b) pCoA and

(c) FeA solutions; subjected to Fenton oxidation at 2 min

(pH 4.7, 25 °C).............................................................................

152

Figure 5.4 Electrostatic potential maps and equilibrium geometries of

(a) CaA; (b) pCoA and (c) FeA as derived from B3LYP/

6-31+G* calculations....................................................................

154

Figure 5.5

Proposed structure of a tetramer of caffeic acid (m/z 715) by

Agha et al., (2009)........................................................................

171

xxiv

Figure 6.1 Typical HPLC-FLD chromatogram (fluorescence detection at

λex = 350 nm and λem = 445 nm) of the melanoidin/HCA

mixture in sucrose solution (15% (w/w)) before and after

modified Fenton oxidation (t = 2 min) at pH 5.6 and 35 °C........

183

Figure 6.2 Typical HPLC-DAD chromatogram (UV/Vis detection at 280

nm) of the melanoidin/phenolic acid mixture in sucrose solution

(15% (w/w)) before and after modified Fenton oxidation

(t = 2 min) at pH 5.6 and 35 °C. 1 = CaA, 2 = pCoA, 3 = FeA.

184

Figure 6.3 Perturbation plot for (%) melanoidin degradation. Coded

values are shown for each factor: melanoidin (A); total HCA

(B); pH (C); FeSO4·7H2O dose (D) and AlCl3·6H2O dose (E);

and refer to actual values listed in Table 6.1................................

191

Figure 6.4 Contour plots of melanoidin degradation (%) as a function of

(a) melanoidin and AlCl3·6H2O dosage; (b) pH and

FeSO4·7H2O dosage. Variables: melanoidin (1,500 mg/L);

total HCA (150 mg/L); pH (5.25); FeSO4·7H2O dosage (389

mg/L) and AlCl3·6H2O dosage (200 mg/L).................................

192


(a) pH and AlCl3·6H2O dosage; (b) FeSO4·7H2O dosage and

AlCl3·6H2O dosage. Variables: melanoidin (1,500 mg/L); total

HCA (150 mg/L); pH (5.25); FeSO4·7H2O dosage (389 mg/L)

and AlCl3·6H2O dosage (200 mg/L)............................................

193

Figure 6.6 Perturbation plot for (%) total HCA degradation. Coded values

are shown for each factor: melanoidin (A); total HCA (B); pH

(C); FeSO4·7H2O dose (D) and AlCl3·6H2O dose (E); and refer

to actual values listed in Table 6.1...............................................

195

Figure 6.7

Contour plots of total HCA degradation (%) as a function of

(a) melanoidin and pH; (b) melanoidin and FeSO4·7H2O

dosage. Variables: melanoidin (1,500 mg/L); total HCA

(150 mg/L); pH (5.25); FeSO4·7H2O dosage (389 mg/L) and

AlCl3·6H2O dosage (200 mg/L)...................................................

196

xxv

Figure 6.8 Contour plots of total HCA degradation (%) as a function of

(a) total HCA and FeSO4·7H2O dosage; (b) total HCA and


HCA (150 mg/L); pH (5.25); FeSO4·7H2O dosage (389 mg/L)

and AlCl3·6H2O dosage (200 mg/L)............................................

197

Figure 6.9 Three-dimensional surface plots of decolourisation (%) as a

function of (a) melanoidin and AlCl3·6H2O dosage; (b) total

HCA and FeSO4·7H2O; (c) total HCA and AlCl3·6H2O dosage;

and (d) FeSO4·7H2O and AlCl3·6H2O. Variables: melanoidin

(1,500 mg/L); total HCA (150 mg/L); pH (5.25); FeSO4·7H2O

dosage (389 mg/L) and AlCl3·6H2O dosage (200 mg/L).............

199

Figure 7.1 Sugar Research Institute designed batch settling kit.................... 209

Figure A2.1 High-performance LC-DAD chromatograms (UV/Vis detection

at 280 nm) of the HCA mixture subjected to Fenton oxidation

at 2 min (pH 4.7, 25 °C)…………...……………………………

259

Figure A2.2 Total ion chromatogram (negative ion mode ESI-MS) of the

HCA mixture subjected to Fenton oxidation at 2 min

(pH 4.7, 25 °C).….……………....………………...……………

260

Figure A2.3 Gas chromatogram of a SPE extract of the HCA mixture

subjected to Fenton oxidation at 2 min (pH 4.7, 25 °C).……......

260

Figure A2.4 Negative ion mode ESI-MS full-scan spectrum relevant to the

dimer product arising from the Fenton oxidation of FeA,

[M]– = 385.1 Da …...........…...……………….…………………

261


tetramer product arising from the Fenton oxidation of CaA,

[M]– = 715.2 Da ……………………….……..…………………

261

Figure A3.1 Normal probability plots of residuals for fitted model using

(a) melanoidin and (b) total HCA degradation data after power

transformation.….………………………………….……………

266

Figure A3.2

Box-Cox plots of (a) melanoidin and (b) total HCA degradation

data for the determination of the optimised power transformed

response surface models.………….….…………………………

267

xxvi

Figure A3.3 Plots of predicted response and experimental (actual) values for

the degradation (%) of (a) melanoidin and (b) total HCA.……...

268

Figure A3.4 Plot of predicted response and experimental (actual) values for

the decolourisation (%).…………………………………….…...

269

xxvii

List of Tables

Table 2.1 Properties of colourants in cane juice (Davis, 2001a).................... 32

Table 2.2 Colour analyses of milled juice at pH 7.0 (Smith et al., 1981)....... 33

Table 2.3 Comparison of colour at pH 7.0 and 9.0 from process streams of

a typical sugar mill (Smith et al., 1981)..........................................

34

Table 2.4 Decolourisation processes on colourants types existing in juice as

adapted from Davis (2001b)...........................................................

37

Table 3.1 Colour of factory cane juices recorded at pH 7.0........................... 63

Table 3.2 Phenolic acids and HMF (mM on dry content) by HPLC-DAD of

sugar cane juices using Method A..................................................

65

Table 3.3 Phenolic acids (mM on juice) and HMF by HPLC-DAD of PJs

using Method A...............................................................................

66


sugar cane juices using Method B..................................................

69

Table 4.1 Volumes of reagents (mM) used for the degradation of CaA......... 76

Table 4.2 Coded and actual values of the experimental design for Design 1. 81

Table 4.3 Coded and actual values of the experimental design for Design 2. 82

Table 4.4 Analysis of variance (ANOVA) results for response surface

quadratic model terms for CaA degradation...................................

89

Table 4.5 Regression diagnostics for the response surface quadratic model

for CaA degradation........................................................................

90

Table 4.6 Optimised conditions under specified constraints for the

degradation of CaA and model verification....................................

97

Table 4.7 Model verification of optimised conditions under randomly

specified constraints for CaA degradation......................................

98

Table 4.8 Model verification of optimised conditions in synthetic sugar

solutions under specified constraints of selected various sugar

process streams for CaA degradation.............................................

99

Table 4.9 Results of ANOVA for model terms of the response surface

reduced quadratic model for CaA degradation...............................

109

xxviii


reduced quadratic model for pCoA degradation.............................

110


reduced quadratic model for FeA degradation................................

111


reduced quadratic model for total HCA degradation......................

112

Table 4.13 Wavenumbers (cm–1) of selected bands from ATR-FTIR spectra

of CaA solution and CaA mixtures containing Fe(II) and/or

sucrose at pH 5.5 and 25 °C............................................................

121

Table 4.14 X-ray diffraction data of the precipitate formed between CaA and

Fe(II) at pH 5.5 and 25 °C..............................................................

126


degradation of total HCA (200 mg/L) and model verification.......

128

Table 5.1 Reaction products formed from the Fenton oxidation of HCAs

detected by LC/MS.........................................................................

146

Table 5.2 Contents of organic acids (mM) by HPIEC of individual and

combined HCA mixtures................................................................

149


detected by GC/MS.........................................................................

151

Table 5.4 Electron density distribution of carbon atoms in HCA molecules. 155

Table 6.1 Coded and actual values of the experimental design...................... 182


reduced two-factor interaction model for melanoidin degradation.

187


reduced two-factor interaction model for total HCA degradation..

188


reduced quadratic model for decolourisation..................................

189


degradation of melanoidin (2,000 mg/L) and total HCA (200

mg/L) in sucrose solution (15% (w/w)) at 35 °C; and model

verification......................................................................................

201

xxix


decolourisation of synthetic juice mixtures containing

melanoidin (2,000 mg/L), HCA (200 mg/L) and sucrose

(15% (w/w)) at 35 °C; and model verification................................

202

Table 7.1 Operating parameters for ICP-OES analyses.................................. 211

Table 7.2 Clarification performance results on clarified No. 2 mill juices

from the Tully Sugar Mill trials......................................................

212

Table 7.3 Inorganic ion composition results on clarified No. 2 mill juices

from the Tully Sugar Mill trials......................................................

213

Table 7.4 Colour results on clarified No. 2 mill juices from the Tully Sugar

Mill trials.........................................................................................

214

Table 7.5 Clarification performance results on clarified factory juices from

the Isis Central Sugar Mill trials.....................................................

216

Table 7.6 Inorganic ion composition results clarified factory juices from

the Isis Central Sugar Mill trials.....................................................

217

Table 7.7 Purity and reducing sugar results on clarified factory juices from

the Isis Central Sugar Mill trials............................................

219

Table 7.8 Colour results on clarified factory juices from the Isis Central

Sugar Mill trials..............................................................................

220

Table 7.9 Prices of additives in bulk quantities used for the modified

Fenton process................................................................................

222

Table A1.1 Experimental design and results for % CaA degradation

(i.e., Design 1)…………………………………………………….

236

Table A1.2 Sucrose and reducing sugar results on selected tests at t = 2 min

(i.e., Design 1)…………………………………………………….

241

Table A1.3 Experimental design and results for % CaA, % pCoA, % FeA

and % total HCA degradation (i.e., Design 2)……………………

243

Table A1.4 Sucrose and reducing sugar results at t = 2 min (i.e., Design 2)…. 245

Table A2.1 Geometry optimisation, charges and bond order computational

calculations of CaA……………………………………………….

247

Table A2.2

Geometry optimisation, charges and bond order computational

calculations of pCoA……………………………………………...

251

xxx


calculations of pCoA……………………………………………...

255

Table A2.4 Sucrose and reducing sugar results of Fenton-mediated reactions

of sucrose at t = 2 min…………………………………………….

259

Table A3.1 Experimental design for % total HCA, % melanoidin degradation

and decolourisation…….………………………………………....

262

Table A3.2 Results for % total HCA, % melanoidin degradation and

decolourisation…………………………………………………....

264

xxxi

List of Abbreviations and Nomenclature

Acronyms

BSES Bureau of Sugar Experiment Stations

CA California, USA

CO Colorado, USA

CT Connecticut, USA

ICDD International Centre for Diffraction Data

IL Illinois, USA

MA Massachusetts, USA

MD Maryland, USA

MN Minnesota, USA

MO Missouri, USA

NSW New South Wales, Australia

QLD Queensland, Australia

QSL Queensland Sugar Limited

QUT Queensland University of Technology

SRI Sugar Research Institute

UK United Kingdom

UQ University of Queensland

USA United States of America

VIC Victoria, Australia

WI Wisconsin, USA

Scientific Acronyms

2EJ Second Expressed Juice

2FI Two-Factor Interaction

3D Three-Dimensional

3EJ Third Expressed Juice

4EJ Fourth Expressed Juice

xxxii

ALS Automatic Liquid Sampler

ANOVA Analysis of Variance

AR Analytical Research

AOP Advanced Oxidation Process

ATR Attenuated Total Reflectance

B3 Becke’s Three Parameters (Functional)

CaA Caffeic Acid

CCD Central Composite Design

CV Coefficient of Variance

DAD Diode-Array Detection

DFT Density Functional Theory

DH Degree of Hydrolysis

DOE Design of Experiments

DS Dry Substance

EI Electron Impact

ESI Electrospray Ionisation

ESJ Evaporator Supply Juice

FeA Ferulic Acid

FEJ First Expressed Juice

FES Final Evaporator Syrup

FHLJ Flocculated Heated Limed Juice

FLD Fluorescence Detection

FTIR Fourier Transform Infrared

GC Gas Chromatography

HADP Hexose Alkaline Degradation Product

HCA Hydroxycinnamic Acid

HF Hartree-Fock

HJ Heated Juice

HMF Hydroxymethylfurfural

HMW High Molecular Weight

HPAEC High-Performance Anion Exchange Chromatography

HPIEC High-Performance Ion Exclusion Chromatography

HPLC High-Performance Liquid Chromatography

xxxiii

ICUMSA International Commission for Uniform Methods of Sugar Analysis

IJ Incubated Juice

IV Indicator Value

LC Liquid Chromatography

LJ Limed Juice

LMW Low Molecular Weight

LYP Lee-Yang-Parr (Functional)

MJ Mixed Juice

MRP Maillard Reaction Product

MRSM Multi-Response Surface Methodology

MS Mass Spectrometry

MS/MS Tandem Mass Spectrometry

MW Molecular Weight

PAD Pulse Amperometric Detection

pCoA p–Coumaric Acid

PJ Primary Juice

PPO Polyphenol Oxidase

PRESS Predicted Residual Sum of Squares

Q Quadrupole

RI Refractive Index

RS Raw Sugar

RSD Relative Standard Deviation

RSM Response Surface Methodology

SPE Solid Phase Extraction

SS Sum of Squares

TIC Total Ion Chromatography

TOF Time-of-Flight

TSS Total Soluble Solids

UV Ultraviolet

UV/Vis Ultraviolet/Visible

VWD Variable Wavelength Detector

XRD X-ray Powder Diffraction

xxxiv

Nomenclature

2° secondary

3° tertiary

A absorbance

df degrees of freedom

E1 early-time waveform

E2 intermediate-time waveform

E3 late-time waveform

e exponential

ε random error term

i.d. internal diameter

k number of factors

L luminescence

ln natural logarithm

λ wavelength

λem emission wavelength

λex excitation wavelength

m– prefix (meta–) for substituents on 1,3-positions of aromatic compounds

m/z mass-to-charge ratio

n number of experiments

o– prefix (ortho–) for substituents on 1,2-positions of aromatic compounds

p p-value for the probability of obtaining a test statistic

p– prefix (para–) for substituents on 1,4-positions of aromatic compounds

π– prefix (pi–) for representing covalent chemical bonds

pKa acid dissociation constant

R2 coefficient of determination

T temperature

t time

tR retention time

xxxv

Units

Å Ångström

AU absorbance units

°Bx degree Brix

°C degree Celsius

cc cubic centimetre

cm centimetre

Da dalton

eV electron volt

g gram

h hour

hp horsepower

IU ICUMSA units

K Kelvin

kg kilogram

kPa kilopascals

kV kilovoltage

kW kilowatt

LU luminescence units

M molarity

mA milliampere

mAU milliabsorbance units

mg milligram

min minute

mL millilitre

mM millimolarity

mm millimetre

MΩ megaohm

μA microampere

μL microlitre

μm micrometre

nm nanometre

ppm parts per million

xxxvi

psig pound-force per square inch gauge

rpm revolutions per minute

s second

t tonne

TU turbidity units

V voltage

% (v/v) volume/volume percent

% (w/w) weight/weight percent

1

CHAPTER 1

General Introduction

1.1 Background and Motivation........................................................... 2

1.2 Research Problem............................................................................ 3

1.3 Aims and Objectives........................................................................ 4

1.4 Scope of this Thesis.......................................................................... 5

2

1.1 Background and Motivation

Sugar is an important commodity in world agricultural trade. It is mainly

sucrose, a disaccharide made up of glucose and fructose, and is sourced from either

sugar cane (Saccharum officinarum L.) or sugar beet (Beta vulgaris L.). Commonly

referred to as table or granulated sugar, its use in the food and beverage industries is

widespread. Sugar obtained from sugar cane contributes to 70% of the world’s sugar

production. However, unlike sugar beet, which is primarily sold as white sugar, a

plethora of sugar products are produced from sugar cane (e.g., raw sugar, syrup and

molasses).

Australia produces approximately 4.5 million tonnes of raw sugar per year, of

which 85% is exported (Canegrowers, 2012). This contributes around $A1.7 billion

to Australia’s export earnings (QSL, 2011). The ongoing fluctuating international

value of sugar continues to stress the viability of Australian sugar mills and threaten

the reliance of regional communities on the industry. A consequence of the increased

competition amongst sugar producers in world trade is an increased focus on the

delivery of high quality sugar. In Australia, high quality raw sugar attracts a premium

value of about $A7 per tonne of sugar (QSL, 2011).

One of the most important parameters in sugar quality is colour. Australian

raw sugars are considered to be of high quality with respect to this parameter, but

there is room for improvement. However, some raw sugars produced in both

Australia and overseas are relatively difficult to decolourise by sugar refiners and tend

to develop colour during storage.

The costs of sugar refining increase with the amount of colouring matter in the

raw sugar feedstock. Therefore, given a choice, sugar refineries select low colour raw

sugar from the markets at a price premium. Sugar refineries are not wanting to affine

low quality raw sugars, as affination is expensive due to the high use of fossil fuels

for energy to operate the fugals. A reduction of colour in raw sugar or a cheap and

effective method of removal in processing would lead to lower refining costs.

3

1.2 Research Problem

Numerous technologies have been developed over the decades to achieve

efficient and effective decolourisation of sugar cane process streams at a reasonable

cost, in order to produce low coloured raw sugar. Apart from the crystallisation

process, there are very few technologies and modifications to current processes that

can significantly and economically reduce colour, except for the use of sulfur dioxide

(SO2) (Paton, 1992; Bento, 1999; Godshall, 1999). The use of SO2 via the sulfitation

process for the production of plantation white sugar is discouraged in many countries

because of the health risks surrounding the consumption of contaminated sugar

containing residual sulfur (6–30 mg/kg) (Steindl and Doherty, 2005). The options

that are in current use in Australian sugar factories for colour removal in raw sugar

include double purging (i.e., washing) of sugar crystals and modification of

crystallisation boiling schemes. These treatment options are not so effective with

high molecular weight (HMW) colourants and require refining in order to obtain

white sugar (Lindeman and O'Shea, 2001).

Different methods that have been trialled to treat sugar cane process streams

for colour removal include clarification techniques (Eggleston et al., 2003), air

flotation (Echeverri and Rein, 2006), membrane filtration (Farmani et al., 2008),

chemical precipitation (Doherty et al., 2003), ion exchange resins (Broadhurst and

Rein, 2003) and activated carbon adsorption (Mudoga et al., 2008). A major

disadvantage that sugar manufacturers face is that most of the aforementioned

processes are colour selective and are not effective in removing certain types of

colourants. To overcome this, combinations of two or more processes are usually

required to produce the best low colour raw sugar (Olivério et al., 2010). However,

the option of using combinations of technologies is expensive and not viable for raw

sugar manufacturers. In addition, sugar manufacturers have also reduced the amount

of extraneous matter entering into the factory in an attempt to reduce the amount of

colour in the final raw sugar product.

In the last decade or so, there has been an increasing trend towards the

evaluation of chemical additives as alternatives to reduce or inhibit colour formation

during sugar processing. These include the use of sulfurated and chlorinated

compounds (Saska et al., 2010), ozone (Moodley et al., 1999), hydrogen peroxide

4

(H2O2) (Mane et al., 2000) and ferric ion (Fe(III)) in conjunction with endogenous

proteins (Madsen and Day, 2010). Advanced oxidation processes (AOPs) are gaining

focus as alternatives to conventional methods for the treatment of organic dyes

(Koprivanac et al., 2005) and industrial wastewaters (Pera-Titus et al., 2004).

However, these technologies have received limited attention to not only decolourise

sugar process streams, but also remove impurities that may affect processing. An

example of this is the activation of H2O2 using ferrous iron (Fe(II)), typically referred

to as the Fenton oxidation process. It is an attractive process for its low capital costs,

low toxicity of reagents and ease of application.

In this context, this present study builds on this line of research by examining

the potential decolourisation and oxidative degradation of colourants and colour

precursor compounds in water, synthetic juice solutions and sugar cane factory juices

using the Fenton oxidation and related processes.

1.3 Aims and Objectives

The overarching aim of the project was to develop a cost-effective

decolourisation process for effective removal of colourants and colour precursors

from sugar cane process streams using the Fenton oxidation process.

The specific objectives of the project were to:

Determine the colour and composition of phenolic acid compounds (i.e.,

colour precursors) present in different juice types.

Optimise process parameters and develop models for the removal of colour

precursors (e.g., hydroxycinnamic acids) and synthetic colourants using the

Fenton process.

Propose mechanisms for the degradation of hydroxycinnamic acids by the

Fenton process.

Evaluate the decolourisation efficiency of the Fenton process on factory juice.

A preliminary cost benefit analysis was also conducted to assess the benefits

of the developed Fenton technologies to remove colour during raw sugar manufacture.

5

1.4 Scope of this Thesis

There are limited reports in the literature with respect to the use of the Fenton

process to treat sugar process streams. There are also gaps in the literature with

regards to the degradation mechanisms of colour precursor compounds using the

Fenton process. Therefore, this thesis examined the use of the Fenton oxidation

process and variants of this process as potential technologies for the removal of

colourants and colour precursors in aqueous and sugar solutions. This thesis is thus

arranged in the following manner.

Chapter 1 provides the background and motivation for the work, the research

problem and the specific objectives of the project.

Chapter 2 covers a comprehensive literature review on the types, origins and

reactions of colourants in the processing of sugar cane; the formation of colour during

processing of sugar cane to produce raw sugar; and discusses known and potential

sugar decolourisation technologies.

Chapter 3 provides a study on the colour and phenolic acid composition of

sugar cane juices processed in Australian sugar factories. Colour content does not

only differ from region to region but also because of differences in cane variety, soil

type, climate and harvesting methods. The standard method used for the

determination of colour precursors is compared to a modified method developed in

the project.

Chapter 4 presents an exhaustive and comprehensive analysis on the effects of

the Fenton oxidation process on a selected group of colour precursor compounds

(viz., caffeic, p–coumaric and ferulic acids). The use of experimental design coupled

with regression modelling through multivariate statistics were used for the

optimisation of the operating parameters.

The Fenton oxidation process is capable of mineralising organic compounds

(i.e., decomposition to carbon dioxide (CO2) and water (H2O)) through reactions

involving free radicals. However, depending on the operating conditions, this may

not imply complete mineralisation. Chapter 5 evaluates the oxidation products

obtained from the Fenton process using several chromatographic and spectroscopic

6

techniques. Attempts were made to determine the degradation pathways of selected

colour precursors.

Complex synthetic juice solutions involving more than one type of colourant

group have been investigated. Mixtures containing a synthetically-made factory

produced colourant (i.e., melanoidin) and hydroxycinnamic acids were degraded and

decolourised using the Fenton and modified Fenton processes. The results from this

work are presented in Chapter 6.

On the basis of the results obtained from Chapters 3 to 6, the developed

technologies and their optimised constraints were then trialled on factory sugar cane

process streams, as shown in Chapter 7. A selected number of juice streams from

Australian sugar factories were tested under laboratory scale conditions. A

preliminary financial analysis based on the indicated benefits and costs of additives on

factory process streams was conducted and discussed in this chapter.

Chapter 8 summarises the overall findings of the works carried out throughout

this project and provides recommendations for future work.

7

References

Bento, L. S. M. (1999). Study of colour formation during carbonation in cane sugar

refining using GPC with a ELS detector. Proceedings of the AVH Association

(pp. 23-27). Reims, France.

Broadhurst, H. A., & Rein, P. W. (2003). Modeling adsorption of cane-sugar solution

colorant in packed-bed ion exchangers. AIChE Journal, 49(10), 2519-2532.

Canegrowers (2012). Canegrowers Australia Annual Report 2011/2012. Tingalpa,

QLD, Australia: Harding Colour.

Doherty, W. O. S., Fellows, C. M., Gorijan, S., Senogles, E., & Cheung, W. H.

(2003). Flocculation and sedimentation of cane sugar juice particles with

cationic homo- and copolymers. Journal of Applied Polymer Science, 90(1),

316-325.

Echeverri, L. F., & Rein, P. W. (2006). Numerical study of the flow in air flotation

syrup clarifiers. Proceedings of the South African Sugar Technologists'

Association, 80, 378-390.

Eggleston, G., Monge, A., & Ogier, B. E. (2003). Sugarcane factory performance of

cold, intermediate, and hot lime clarification processes. Journal of Food

Processing and Preservation, 26, 433-454.

Farmani, B., Haddadekhodaparast, M. H., Hesari, J., & Aharizad, S. (2008).

Determining optimum conditions for sugarcane juice refinement by pilot plant

dead-end ceramic micro-filtration. Journal of Agriculture, Science and

Technology, 10, 351-357.

Godshall, M. A. (1999). Removal of colorants and polysaccharides and the quality of

white sugar. Proceedings of the AVH Association (pp. 28-35). Reims, France.

Koprivanac, N., Kušić, H., Vujević, D., Peternel, I., & Locke, B. R. (2005). Influence

of iron on degradation of organic dyes in corona. Journal of Hazardous

Materials, B117, 113-119.

Lindeman, P. F., & O'Shea, M. G. (2001). High molecualr weight (HMW) colorants

and their impact on the refinability of raw sugar. A study of Australian and

overseas raw sugars. Proceedings of the Australian Society of Sugar Cane

Technologists, 23, 322-329.

Madsen, L. R., II, & Day, D. F. (2010). Iron mediated clarification and

decolourisation of sugarcane juice. Proceedings of the International Society of

Sugar Cane Technologists, 27, 1-13.

Mane, J. D., Phadnis, S. P., Jambhale, D. B., & Yewale, A. V. (2000). Mill scale

evaluation of hydrogen peroxide as a processing aid: quality improvement in

plantation white sugar. International Sugar Journal, 102(1222), 530-533.

8

Moodley, M., Davis, S. B., & Adendorff, M. W. (1999). Full scale decolourisation

trials with ozone. International Sugar Journal, 101, 165-171.

Mudoga, H. L., Yucel, H., & Kincal, N. S. (2008). Decolorization of sugar syrups

using commercial and sugar beet pulp based activated carbons. Bioresource

Technology, 99, 3528-3533.

Olivério, J. L., Boscariol, F. C., Mantelatto, P. E., Ciambelli, J. R., Gabardo, H., &

Oliveira, A. A. (2010). DRD–Dedini Refinado Direto (Dedini Direct Refined)

improvements in refined and crystal white sugar production. Proceedings of

the International Society of Sugar Cane Technologists, 27, 1-13.

Paton, N. H. (1992). The origin of colour in raw sugar. Proceedings of the Australian

Society of Sugar Cane Technologists, 14, 8-17.

Pera-Titus, M., García-Molina, V., Baños, M. A., Giménez, J., & Esplugas, S. (2004).

Degradation of chlorophenols by means of advanced oxidation processes: a

general review. Applied Catalysis, B: Environmental, 47, 219-256.

QSL (2011). Key Facts | Queensland Sugar. Retrieved March 14, 2013, from

http://www.qsl.com.au/about-qsl/key-facts

Saska, M., Zossi, S., & Liu, H. (2010). Colour behaviour in cane juice clarification by

defecation, sulfitation and carbonation. Proceedings of the International


Steindl, R. J., & Doherty, W. O. S. (2005). Syrup clarification for plantation white

sugar to meet new quality standards. International Sugar Journal, 107(1282),

581-589.

9

CHAPTER 2

Literature Review

2.1 Introduction..................................................................................... 10

2.2 Colourants in Sugar Process Streams............................................ 10

2.2.1 Naturally Occurring Colourants......................................... 12

2.2.2 Factory Produced Colourants............................................. 16

2.3 Reactivity of Colourants during Sugar Manufacturing............... 18

2.3.1 Enzymatic Browning............................................................ 18

2.3.2 Non-enzymatic Oxidation.................................................... 20

2.3.3 Maillard Reaction................................................................ 21

2.3.4 Caramelisation.................................................................... 24

2.3.5 Hexose Alkaline Degradation............................................. 27

2.3.6 Conversion of Anthocyanins to Chalcones.......................... 27

2.3.7 Biochemical Precursors of Flavonoids............................... 30

2.4 Colour in Sugar Process Streams................................................... 31

2.4.1 Effects of Temperature on Colour Formation..................... 35

2.5 Sugar Decolourisation Technologies.............................................. 37

2.5.1 Current Technologies.......................................................... 37

2.5.2 Decolourisation using Chemical Additives......................... 38

2.5.3 Novel and Potential Technologies....................................... 42

10

2.1 Introduction

The development of colour during sugar processing is a common problem for

the sugar manufacturing industry. Juices and syrups produced as a result of

processing contain compounds that end up in the sugar crystal. This chapter presents

an overview of the previous work on sugar colour and provides the essential

background for the current research. A review of the literature on the properties of

colourants, their behaviour during processing and evaluation of decolourisation

technologies is described in this chapter. The review provides one understanding of

the fundamental mechanisms of colour formation in sugar cane processing.

2.2 Colourants in Sugar Process Streams

A representation of the typical sugar manufacturing process in Australia is

shown in Figure 2.1. Sugar cane is harvested and cut on a seasonal basis. Harvested

sugar cane is transported in large containers or bins to the sugar mill. The cane is then

shredded and crushed (i.e., milled) to extract the juice. The juice is incubated and

limed to remove impurities (e.g., starch) that affect subsequent processes and

minimise sucrose inversion. The limed juice is then boiled (≥ 100 °C) and flashed

before a flocculant is added to enhance the bridging of impurity aggregates. The

treated juice is then clarified to separate and remove flocculated impurities, fibre and

soil. Clarified juice then passes to the evaporation stage, where water is removed to

form syrup. In the crystallisation process, the syrup is seeded and the crystals grow in

vacuum pans, followed by separation of crystals by centrifugation. The separated

crystals are washed and then dried to produce raw sugar ready for export or

transferred to a sugar refinery for the production of white sugar.

11

Figure 2.1 Schematic flowchart of the sugar manufacturing process in

Australia.

Colour in sugar process streams consist of a complex mixture of compounds.

They are introduced naturally from the cane plant or produced during processing in

the factory. The compounds formed have different molecular weights, chemical

structures and properties as a result of degradation and polymerisation reactions

caused by changes in process parameters such as pH and temperature. The colourants

that are difficult to remove are mainly hydrophobic in nature and persist throughout

the sugar manufacturing process occluding within the sugar crystals. Moreover, their

behaviour and reactivity at various stages of the sugar manufacturing process is

extremely complex. Therefore, it is important to understand the process parameters

that contribute to the formation of colour in order to develop technologies suitable for

the subsequent removal of colour during processing.

Sugar CaneCane

HarvestingMilling Incubation

ClarificationEvaporationCrystallisationRaw Sugar

Exported to Consumers

Transferred to Refinery

12

2.2.1 Naturally Occurring Colourants

Chlorophylls and Carotenoids

Sugar cane pigments are predominantly made up of chlorophylls, carotenoids

(carotenes and xanthophylls) and flavonoids. These colourants are present in

expressed juices after the milling of cane. Extraneous matter such as the tops and

leaves of the sugar cane plant contribute significantly to colour in juice (Mersad et al.,

2003). Colloidal in nature, chlorophylls and carotenoids are insoluble in water.

Therefore, they do not contribute to the colouring of the final product as they are

easily removed during clarification.

Flavonoids

Flavonoids are soluble and weakly acidic in nature and persist throughout the

milling and refining processes. These compounds are essential for the growth of the

sugar cane plant. However, their presence in processing significantly impacts on the

colouring of raw sugar. Flavonoids contribute up to a third of the colouring in raw

sugar according to Smith and Paton (1985). This amount can considerably rise with

juices expressed from whole green cane crop that contain tops and leaves. The

colouring of raw sugar from flavonoids is attributable to the occlusion of flavonoid

glycosides in the sugar crystals during crystallisation. These naturally occurring

compounds are divided into various subgroups such as flavones, flavanols and

anthocyanins and only differ in the numbering and positioning of hydroxyl groups on

the C6–C3–C6 flavonoid backbone structure. A summary of these structures is

presented in Figure 2.2 (Ververidis et al., 2007).

13

Figure 2.2 Flavonoid structures found in sugar process streams and products.

Examples are given under the general chemical structures

(Ververidis et al., 2007).

Phenolic Compounds

The term phenolic comprises a wide range of compounds which possess an

aromatic ring with one or more hydroxyl groups. Their presence is widespread

throughout the plant kingdom. Phenolics in nature can exist in their free and bound

forms, as esters or glycosides (e.g., flavonoids). Mainly colourless, phenolics are

endogenous to the cane and are introduced into the sugar process streams after the

crushing of cane. Subsequently, these phenolics may participate in enzymatic,

complexation or polymerisation reactions yielding coloured compounds which

survive throughout the milling process. A summary of the phenolic compounds is

described in Figure 2.3 (Harborne, 1989; Vermerris and Nicholson, 2006).

O

3

5

6

7

82'

3'

4'

5'

6'

O

O

35

6

7

82'

3'

4'

5'

6'

O

O

OH35

6

7

82'

3'

4'

5'

6'

35

6

7

8

2'

3'

4'

5'

6'

O

O

O

OH35

6

7

82'

3'

4'

5'

6'

O

O

35

6

7

82'

3'

4'

5'

6'

Anthocyanidins (Flavylium ion)

3,5,7,3’,5’–OH: Cyanidin

3,5,7,4’–OH, 3’–OCH3: Peonidin

3,5,7,4’–OH, 3’,5’–OCH3: Malvidin

3–O–sugar: Anthocyanidin Glycosides

Flavones (2–phenylchromen–4–one)

5,7,4’–OH: Apigenin

5,7,3’,4’–OH: Luteolin

5,7,4’–OH, 3’,5’–OCH3: Tricin

5,7 or 6,8–sugar: Flavones Glycosides

5,7,4’–OH: Kaempferol

5,7,3’,4’–OH: Quercetin

5,7,3’,4’,5’–OH: Myricetin

3,7–O–sugar: Flavonol Glycosides

Flavonols (2–phenylchromen–4–one)

Isoflavones (3–phenylchromen–4–one)

7,4’–OH: Daidzein

5,7,4’–OH: Genistein

7,4’–OH, 6–OCH3: Glycitein

7–O–sugar: Isof lavone Glycosides

Flavanols or Flavan-3-ols

(2–phenyl–3,4–dihydro–2H–chromen–3–ol)

5,7,4’,5’–OH: Catechin

5,7,3’,4’,5’–OH: Gallocatechin

6,8–sugar: Flavanol Glycosides

5,7,4’–OH: Narnigenin

5,7,3’–OH, 4’–OCH3: Hesperetin

3,5,7,4’,5’–OH: Taxifolin (also a flavanonol)

7–O–sugar: Flavanone Glycosides

Flavanone (2,3–dihydro–2–phenylchromen–4–one)

14

Figure 2.3 Structures of phenolics found in sugar process streams and


structures (Harborne, 1989; Vermerris and Nicholson, 2006).

Polyphenolic Compounds

Polymers consisting of multiple phenolic units are termed polyphenols. The

number of repeating phenolic units varies; hence each polymer has a different

molecular weight and structure. The disambiguation of polyphenols is shown in

Figure 2.4. The simplest polyphenols are dimers of the monomeric phenolic units

such as ellagic acid (i.e., gallic acid dimer). Intermediate polyphenols consist of two

or more dimers of monomeric phenolic units (e.g., ellagitannin (Ross et al., 2007)).

The molecular weights and structures of simple and intermediate polyphenols can be

determined. However, this is not possible for complex polyphenols (e.g., lignin)

which consist of repetitive monomeric phenolic units resulting in a macromolecule

with an extremely HMW. In most cases, the molecular structures of these products

are undefined and only approximations can be given.

Basic Phenolics (C6)

2–OH: Catechol

3–OH: Resorcinol

3,5’–OH, 3’–OCH3: Phloroglucinol

Hydroxybenzoic Acids (C6–C1)

2–OH: Salicylic acid

3,4–OH: Protocatechuic acid

3,4,5–OH: Gallic acid

4–OH: p–Coumaric acid

3,4–OH: Caf feic acid

4–OH, 3–OCH3: Ferulic acid

Hydroxycinnamic Acids (C6–C3)

4

3

2

OH

5

6

4

3

2

OHO

5

6

4

3

2

OHO

5

6

4–OH: 4–Hydroxyphenylacetic acid

3,4–OH: 3,4–Dihydroxyphenylacetic acid

4–OH, 3–OCH3: Homovanillic acid

Phenylacetic Acids (C6–C2)

4

3

2

OH

O

5

6

Coumarins (C6–C3)

6–OH: Umbelliferone

6,7–OH: Aesculetin

6–OCH3: Herniarin

Xanthonoids (C6–C1–C6)

1–OH, 7–glu: Euxanthin

1,3,6,7–OH, 2–glc: Mangiferin

3,6,8–OH, 2–OCH3, 1,7–CH2CH(CH3)2: Mangostin

5

8

O O

OH

OH

O

O1

4 5

8

3 6

2 7

Stillbenoids (C6–C2–C6)

3,5–OH: Pinosylvin

3,5,4’–OH: Resveratrol

3,3’,4’–OH, 2–glc: Astringin

3

5

4 2

6

2'

3'

4'

5'

6'

5

4

2O

O

3

Naphthoquinones (C6–C4)

5–OH: Juglone

O

O

1

4 5

8

3 6

2 7

Anthraquinones (C6–C2–C6)

1,8–OH: 1,8–Dihydroxyanthraquinone

1,3,8–OH, 6–OCH3: Emodin

O6'

5'

4'

3'2

3

4

5

6

Chalconoids (C6–C3–C6)

3–OH, 5’–CH3: Methyl hydroxychalcone

15

Figure 2.4 Polymerisation of monomeric gallic acid to polyphenols ellagic

acid and ellagitannin (Ross et al., 2007).

Sugar cane polyphenols include lignins and tannins. Lignin is a complex

macromolecule present in the cell wall of plants. The rigidity of plant stems is

attributable to the presence of lignin with cellulose. Lignin comprises of three

different phenolic units (viz., p–hydroxyphenyl, guaiacyl and syringyl); the

proportions vary according to the type of cane plant and the extraction conditions

(Alves et al., 2012). Tannins are polymeric products of phenolic compounds. They

have the ability to form strongly coloured complexes with proteins to form stable,

hydrophobic co-polymers.

Ellagitannin

Polymer containing 8 gallic acid units

OH

OH

OH

OH

OH OH

O

O

O

O

O

OH

OH

O

OH

OH

OH

OH

OH

OH

OH OH

O

O

O

O

OH OH

OH

OH

OH

OH

O

OO

O

O

O

O

OH

OH

OH

O

O

O

O

OH

OH OH

OH

Ellagic acid

Dimer containing 2 gallic acid units

OH

O

OH

OH

OH

Gallic acid

Phenolic acid monomer

16

Nitrogenous Compounds

The main group of nitrogen-containing compounds present in cane juice are

amino acids and proteins. Proteins are complex HMW compounds made up of amino

acids. The amount of proteins in juice is dependent on the cane variety, soil type and

harvesting conditions. Moreover, the levels of proteins in juice are relatively lower in

sugar cane than in sugar beet. Proteins are of different isoelectric points some of

which are removed during clarification while the remainder persist in the later stages

of processing. Proteins denature and degrade to individual amino acid units as a

result of heat and changes in pH. Amino acids produced from protein denaturation

coupled with those endogenous to the cane plant are not removed during clarification

and can react with reducing sugars via the Maillard reaction to form HMW dark

coloured compounds.

2.2.2 Factory Produced Colourants

Melanins

Polyphenolic products formed by the enzymatic oxidation of phenolic

compounds during processing are called melanins. A typical structure is shown in

Section 2.3.1. The enzymatic browning is catalysed by the polyphenol oxidase (PPO)

enzyme responsible for the conversion of phenols into quinones (Bucheli and

Robinson, 1994). The quinones can then bind to proteins to form coloured polymers

or undergo condensation to form dark colourants.

Melanoidins

Melanoidins by definition are the coloured end products of the Maillard

reaction between an amine (e.g., amino acid) and a carbonyl compound (e.g., reducing

sugar). Also known as the non-enzymatic browning reaction, the reaction

mechanisms are complex, consisting of repetitive condensation, dehydration and

polymerisation reactions resulting in dark brown coloured substances (Rizzi, 1997).

The coloured Maillard reaction products (MRPs) formed are of varying molecular

17

weights, which are dependent on temperature and reaction time. A description on

how a melanoidin is formed is presented in Section 2.3.3.

Aroma Compounds

Aroma compounds are reaction intermediates formed as a result of sucrose

degradation, sucrose fragmentation and amino acid degradation. Some of these

products are similar to those obtained from Maillard and caramelisation reactions.

Intermediate products are capable of further reacting amongst each other to yield

volatile products such as pyrazines, imidazoles and thiophenes. These products act as

precursors of melanoidins since they either possess amino nitrogen or carbonyl

groups, initiating the Maillard reaction.

Caramels

Caramels are produced by the polymerisation of thermally degraded products

of sucrose at high temperatures (Baunsgaard et al., 2001). The products contain

mixtures of oligosaccharides, polysaccharides and coloured matter (Lindeman and

O'Shea, 2001). These colloidal compounds formed have a tendency to remain on the

outer surface of the sugar crystals, which affect the quality of the final raw sugar

product. A description on the formation of caramels is shown in Section 2.3.4.

Hexose Alkaline Degradation Products (HADPs)

Alkaline degradation products of hexose sugars are coloured products formed

as a result of the thermal decomposition of reducing sugars. The end products mainly

consist of carboxylic acids, carbonyl compounds and lower molecular weight (LMW)

polymers, which can lead to inversion of sucrose and further colour formation. The

degradation rate and composition are heavily dependent on temperature, juice pH and

the presence of divalent cations (Coca et al., 2004). The alkaline degradation rate of

hexose sugars is much faster than under acidic conditions. Typical structures of

HADPs are presented in Section 2.3.5.

18

2.3 Reactivity of Colourants during Sugar Manufacturing

2.3.1 Enzymatic Browning

Enzymatic browning is a colour forming reaction involving a phenolic and a

nitrogenous compound, occurring prior to the heating of sugar cane juice to form

melanins. The reaction is likely to take place after crushing and milling of sugar cane

when the juice makes contact with atmospheric air. In general, the reaction involves

an enzyme that acts as a catalyst to oxidise o–diphenolic substrates to

o–benzoquinones (Li et al., 2008). The o–benzoquinone can further react with a

phenolic compound or an amino acid to produce a highly dark coloured condensation

product (i.e., melanins) (Kort, 1979; Riffer, 2000).

The presence of PPO catalyses two reactions: the production of a diphenol

(Singleton, 1987) and the oxidation of the diphenol to an o–benzoquinone (Li et al.,

2008). The first reaction is described in Scheme 2.1, where the monophenol is

oxidised (1) to a diphenol (2). The following reaction (Scheme 2.2) involves the

oxidation of the diphenol (2) to yield o–benzoquinone (3) and water.

OH

(Monophenol)

Phenol

(1)

+O

OH

OH

(Diphenol)

Catechol

(2)

Scheme 2.1

OH

OH O2

O

O

(Quinone)

(3)

2 2 + 2 OH2

(Diphenol)

Catechol

(2)

oBenzoquinone

Scheme 2.2

19

In a separate example, tyrosine, an amino acid present in cane juice, readily

participates in this reaction using the PPO catalyst described in Scheme 2.3 (Wiggins,

1953; Cleary, 1988). Tyrosine (4) undergoes oxidation to dihydroxyphenylalanine

(5). Subsequent catalytic oxidation yields dopaquinone (6). Dopaquinone is

converted to a leuco compound (7) and then oxidised to give dopachrome (8).

Decarboxylation of (8) yields 5,6–dihydroxyindole (9). Further oxidation of (9)

yields indole–5,6–quinone (10) and slower oxidation over time will eventually

produce a melanin (11).

OHNH2

OH

O

(4)

Tyrosine

+O

slow

OHNH2

OH

O

OH

(5)

Dihydroxyphenylalanine

+O

fast

ONH2

OH

O

O

(6)

Dopaquinone

OH

OH

NH

OH

O

(7)

(Leuco Compound)

OH NH

OH

(9)

5,6-Dihydroxyindole

slow

(8)

Dopachrome

+O

fast

O

O

NH

OH

O+ CO2

+O fast

O

O NH

(10)

Indole-5,6-quinone

+O

slow

(11)

(Melanin)

CH NH

CH O

O

Scheme 2.3

20

2.3.2 Non-Enzymatic Oxidation

Non-enzymatic (i.e., chemical) oxidation can occur throughout the sugar

manufacturing process via several reaction pathways with phenolic compounds.

Phenolics that have two or more hydroxyl functional groups on the aromatic ring such

as caffeic acid and its esters (hydroxycinnamic compounds), gallic (hydroxybenzoic

compounds), catechin (flavanols) and malvidin (anthocyanidins) are considered to be

vulnerable to oxidation and produce colour during the manufacturing process of sugar

(Fernandez-Zurbano et al., 1995; Fernández-Zurbano et al., 1998; Kilmartin et al.,

2001). The primary reaction pathway is through the oxidation of phenolics by

reactive oxygen species, catalytically produced from O2 under the presence of

transistion metal ions (viz., copper (Cu) and iron). As shown in Figure 2.5, the

oxidised products are semiquinone radicals and benzoquinones, while the reduction

product of O2, mediated by the redox cycle of Fe(II)/Fe(III) and Cu(I)/Cu(II), is H2O2

(Danilewicz et al., 2008). The quinones formed as a result of oxidation are unstable

and due to their highly electrophilic nature, they can spontaneously react with other

phenolics and amine compounds present in juice to produce coloured polymeric

substances (Oliveira et al., 2011).

R

OH

OH

R

O

OH

Fe(III)

Fe(II)

Cu(I)

Cu(II)

O2

HO2

H2O2

R

O

OH

R

OH

OH

R

O

O

R

O

O

(Diphenol)

(Semiquinone Radical)

(Benzoquinone)

(Benzoquinone)

(Semiquinone Radical)

(Diphenol)

Figure 2.5 Redox chemistry of phenolics under copper and iron to produce

colour forming products as proposed by Danilewicz et al. (2008)

21

Other reaction pathways that may occur during the manufacturing process of

raw sugar include condensation reactions of phenolics with aldehydes

(viz., acetaldehyde) and organic compounds with an aldehyde functional group

(viz., glyoxylic acid). These condensation reactions are common in the wine industry

and are mainly responsible for the colouring and flavouring of wines (Ferreira et al.,

1997; Silva Ferreira et al., 2003). These reactions involve the protonation of an

aldehyde to give a carbocation under acidic conditions, followed by the nucleophilic

addition of the C8 position of the C6-C3 moiety of a flavonoid compound (cf. Figure

2.2) (Li et al., 2008). The intermediate produced is then protonated and can react

with another phenolic compound of any type producing coloured polymers (Fulcrand

et al., 2006).

2.3.3 Maillard Reaction

The Maillard reaction is a non-enzymatic browning process that involves the

reaction of an amino compound with a reducing sugar to produce to a melanoidin. A

common example of the Maillard reaction is described by Cleary (1988) between

glucose and glycine. In this reaction, the formation of a Schiff’s base (or commonly

known as an imine) occurs followed by an Amadori rearrangement to yield an enol.

Scheme 2.4 shows that initial nucleophilic addition occurs where the active

lone pair of electrons on the amine nitrogen atom of glycine (13) attacks the

electrophilic carbonyl carbon of glucose (12) to form a zwitterion (14) (Carey and

Sundberg, 2007). The zwitterion is then converted to an unstable carbinolamine (15).

22

(12)

Glucose

OHH

O

OH

OH

OH

OH

NH2

O

OH+

(13)

Glycine

OH

O

OH

OH

OH

OH

H

NH

OH

O

(14)

(Zwitterion)

OH

OH

OH

OH

OH

H

N

OH

OH

H

O

(15)

(Unstable Carbinolamine)

Scheme 2.4

Nucleophilic addition with a base to (15) and removal of water yields an imine

(16) (Scheme 2.5). The imine (16) undergoes an Amadori rearrangement where the

hydrogen atom bonded to the carbon atom adjacent to the carbon-nitrogen double

bond (C=N) relocates to bond with the nitrogen atom forming an enol (17), as shown

in Scheme 2.6. The Amadori product could also participate in a keto-enol

tautomerism rearrangement to its keto-form (18) (Scheme 2.7). It is also possible for

these products to take part in further colour forming reactions (Belitz et al., 2009).

23

(15)

(Unstable Carbinolamine)

- H2O

+ H2O

(16)

(Imine/Schiff's Base)

OH

OH

OH

OH

OH

N

H

OH

O

OH

OH

OH

OH

OH

H

N

OH

OH

H

O

B

Scheme 2.5

(16)

(Imine/Schiff's Base)

OH

OH

OH

OH

OH

N

H

OH

O

H

(17)

(Enol/Amadori Product)

OH

OH

OH

OH

OH

NH

OH

O

H

Scheme 2.6

(17)

(Enol/Amadori Product)

OH

OH

OH

OH

OH

NH

OH

O

H

(18)

OH

O

OH

OH

OH

NH

OH

O

Scheme 2.7

24

2.3.4 Caramelisation

Under the harsh operating conditions in sugar factories (e.g., elevated

temperatures, acidic pH), caramelisation takes place. The chemistry of caramelisation

is poorly understood due to the complexity of the reactions taken place. Various

general mechanisms have been proposed in the literature and are shown in Scheme

2.8 (Riffer, 1988; Suárez-Pereira et al., 2010).

Heating sucrose syrup at elevated temperatures can form levoglucosan or

decompose to glucose and fructose. These simple sugars form hydroxyl-

methylfurfural (HMF) which is cleaved to yield one equivalent of formic acid and

levulinic acid or react with other volatile compounds to yield melanoidins or

polymeric colourants (Figures 2.6–2.8). Under increasing acidic conditions and

subsequent losses of water (i.e., evaporation) formation of polymers with difructose

dianhydride units can also occur (Madsen, 2006; Suárez-Pereira et al., 2010). Oxygen

does not influence or contribute to further colouring of the caramel formed but could

possibly affect the solubility of the caramel in water or acidic solution (Belitz et al.,

2009).

Scheme 2.8a is an older proposed mechanism of the caramelisation reaction by

Riffer (1988). Thermal degradation of sucrose yields levoglucosan. Dehydration of

levoglucosan yields levoglucoseone. The precursor of HMF, 3,4–dideoxy-

glucosulose–3–ene can be obtained from levoglucosenone. (Daniels and Lohneis,

1997). The latter product can undergo cleavage to yield one equivalent of levulinic

acid and formic acid or react with other volatiles to form melanoidins, coloured

polymers and condensation products (Scheme 2.8d).

25

Sucrose

Heat

H+/H2O

Glucose

Glucooligosaccharides

Fructose

+O

OH

H

OH

OH

H

H

OH OH

HMF

O

OH H

OOther

Volatiles

Fructopyranose

O

H

H

OH

OH

H

H

OH OH

Fructosyl Oxocarbenium CationO

OH

OH

O

- H2O

3,4-Dideoxyglucosulose-3-ene

O

O

O

Levoglucosenone

+ H2O

O

OH

OH

O

OH

Levoglucosan

- H2O

HMF

O

OH H

O

Fructosyl Oxacarbenium Cation

Fructose Disaccharides

Difructose Dianhydride Compounds

(a)

(b)

(d)

(c) (e) (f)

Heat

- H2O

Other

Volatiles

OO

OH

OH O

O

O

O

O

OHCH3

O

O

Levulinic acid

H OH

O

Formic acid

OHCH3

O

O

Levulinic acid

H OH

O

Formic acid

Melanoidins (cf., Figures 2.6-2.7)

Condensation Products (cf., Figure 2.8)

Melanoidins (cf., Figures 2.6-2.7)

Condensation Products (cf., Figure 2.8)

O

OH

HH

H

OH

OH

H OH

H

OH

O

OH

HH

H

OOH

H OH

H

OH

O

H

OH

OH

H

H

OH OH

O

OH

OHH

H

OH

H

OH H

HOH

O

OHOH

OH

OH

Scheme 2.8

26

A more recent proposed mechanism of the caramelisation reaction is shown in

Scheme 2.8b (Suárez-Pereira et al., 2010). Sucrose is hydrolysed and thermally

degraded to glucose and fructose. Glucose is in equilibrium with the

glucooligosaccharides (Scheme 2.8c). On the other hand, fructose may take part in

several different reaction pathways.

In Scheme 2.8d, dehydration of fructose yields HMF which can then further

participate in colour-forming reactions. In both Schemes 8e and 8f, fructose can form

5-membered or 6-membered fructosyl oxacarbenium cations and subsequently to

fructose disaccharides and other difructose dianhydride compounds.

OHOglc

RO

CH3

O

CH3

NR

OR

OH

OH

O glc

O

OH

OR

OH

O glc

R = H, glc or (glc)n

Figure 2.6 An example of a basic melanoidin structure formed from

3–deoxyhexosuloses (Cämmerer et al., 2002).

27

C C C1

N

H

CHOH

CHOH

CH2OH

OH H CHR1

C1

COO

H

C

CH2

CHOR

CHOH

CH2OH

C C

CHOR

CHOH

CH2OH

OH

C1

H

N+

CR1

COOH

C1

C

CH2

CHOR

CHOH

CH2OH

N

H

H HR1 = Amino acid side chain

R = H or sugars

(e.g., amide, ester)

Figure 2.7 An example of a melanoidin polymer formed from

3–deoxyhexosuloses and amino acids proposed by Cämmerer and

Kroh (1995).

O

H

O

OH

O

H

OH

CH3

OO

H

OHO

OOH

H

Aldol Condensation Aldol Condensation

Figure 2.8 Condensation product formed from the reaction of HMF and a

ketone; followed by an additional condensation reaction with a

second equivalent of HMF (Chheda and Dumesic, 2007).

2.3.5 Hexose Alkaline Degradation

Degradation of fructose and glucose under hot alkaline conditions show both

reducing sugars undergo similar reaction pathways and form similar end products

(Yang and Montgomery, 1996; Knill and Kennedy, 2003; Sinnott, 2007). In one

study, over 50 products (including lactic acid, oxalic acid, saccharinic acids, short and

long-chain carboxylic acids) were identified from the degradation of fructose and

glucose in calcium hydroxide solutions (Yang and Montgomery, 1996).

28

The reaction mechanisms are often referred to as the Nef-Isbell-Richards

mechanisms for the degradation of reducing sugars (Sinnott, 2007). There are at least

six reaction steps in this mechanism:

Step 1: Keto-enol tautomerism of a reducing sugar (e.g., glucose, fructose).

Step 2: Enediol deprotonation.

Step 3: Anion isomerisation.

Step 4: Elimination of β–hydroxycarbonyl group.

Step 5: Keto-enol tautomerism.

Step 6: Benzilic acid rearrangement.

Scheme 2.9 illustrates the initial enolisation of fructose (or glucose) (Reaction

Steps 1 and 2) to form a 1,2–enediolate. Elimination of the hydroxyl group (Reaction

Step 4) yields a 1–aldehydo–3–deoxy–2,3–enol. The product undergoes further

tautomerisation (Reaction Step 5) to yield an α–ketoaldehyde followed by addition of

a hydroxide anion. The product then undergoes the final step, a benzilic acid

rearrangement (Reaction Step 6) to yield metasaccharinic acid.

Alternatively, subsequent anion isomerisation of the 1,2–enediolate may also

occur (Reaction Step 3). The two other isomers then participate in the same reactions

(Reaction Steps 4–6) forming isosaccharinic and saccharinic acids respectively.

There are other numerous reactions that may take place under alkaline

conditions such as the Lobry de Bruyn-van Ekenstein transformation, which is the

interconversion between an aldose sugar and a ketose sugar (Hajek et al., 2013). The

reaction steps involved in these reactions are similar to the aforementioned pathways.

29

OH

O

OH

OH

OH

OH

O-

OH

OH

OH

OH

OH

O

OH

OH

OH

OH

O

OH

OH

OH

O-

H OH

OH

OH

OH

OH

O-

O

Metsaccharinic

Acid

Fructose

(Step 1, 2)

O

O

OH

OH

OH

(Step 4) (Step 5) (Step 6)OH+

(Step 3)

OH

O-

OH

OH

OH

OH

O

O

OH

OH

OH

OH

O-

OH

OH

O

OH

Isosaccharinic

Acid

OH

O

OH

OH

O

(Step 4) (Step 5) (Step 6)+

OH

OH

O-

O

OH

OH

(Step 3)

OH

OH

OH

OH

OH

O-

CH2

OH

OH

OH

O

OH

OH

O-

OH

OH

O

CH3

OH

Saccharinic

Acid

CH3

O

OH

OH

O

OH

(Step 4) (Step 5) (Step 6)+

OH

OH

O-

O

OHCH3

CH3

OH

OH

Scheme 2.9

2.3.6 Conversion of Anthocyanins to Chalcones

When sugar cane juice is heated at pH 7.0, anthocyanins are decomposed to

yield one equivalent of a chalconoid and a coumain-glucoside (Smith and Paton,

1985). An example of this is depicted in Scheme 2.10 as the glycoside of malvidin

(19) is degraded by heat to its corresponding chalcone (20) and a coumarin-glucoside

(21) is formed.

30

O+

O

Sugar

OCH3

OH

OCH3

OH

OH

O

Sugar

OCH3

OH

OCH3

OH

OH

OOHO

O

Sugar

OOH

OH

+

(19)

Heat

Malvidin

(20)

Chalcone

(21)

Coumarin-Glucoside

Scheme 2.10

2.3.7 Biochemical Precursors of Flavonoids

There is a relation between phenolic compounds and flavonoids. Cinnamic

acid derivatives are biochemical precursors of flavonoids (Smith and Paton, 1985).

For example, in Scheme 2.11, a tricin aglycone molecule (22) is decomposed to yield

two products, phloroglucinol (23) and sinapic acid (24). Further oxidation of (24)

yields syringic acid (25). Other phenolic acid derivatives such as caffeic and

p–hydroxybenzoic acids are related to luteolin and apigenin aglycones respectively as

they undergo similar reaction mechanisms.

O

OCH3

OH

OCH3

O

OH

OH

OH

OH

OHOCH3

OH

OCH3O

OH+

OCH3

OH

OCH3

OH

O

(22)

(25)

Tricin Aglycone

(23)

Phloroglucinol

(24)

Sinapinic Acid

Syringic Acid

+O

Scheme 2.11

31

2.4 Colour in Sugar Process Streams

As reported in the previous section, the formation of colourants produced

during factory processing is mainly due to sugar degradation reactions. Reducing

sugars, such as glucose and fructose, formed by the inversion of sucrose, play an

important role in the formation of colour. These sugars degrade due to changes in

operating conditions such as pH and temperature to form highly reactive

intermediates, which undergo condensation and polymerisation reactions to form

highly coloured polymers. Colour precursors are of interest as they are not removed

during juice clarification and polymerise to HMW coloured polymers and

subsequently contribute to the colour in raw sugar (Lindeman and O'Shea, 2001).

A wide range of cane pigments and natural colourants are introduced into the

manufacturing process as a result of milling and crushing of harvested cane. The cane

plant primarily consists of LMW compounds that contribute approximately 30% of

the colouring in raw sugar (Paton, 1992). The remaining 70% is attributable to

colourants produced in the factory, mostly polymeric and of HMW with different

chemical structures and properties. Lindeman and O’Shea (2001) reported that

50–60% of colourants by weight were of HMW and its contribution of these to the

total colour in the final product, based on a standard spectrophotometric procedure at

420 nm, was approximately twice that of LMW colourants.

Generally in Australia and most other parts of the world, colour is measured at

pH 7.0, however the colour at pH 7.0 is the least stable. Moreover, additional

information about the nature of the colourants present can be obtained by taking

measurements at pH 4.0 and pH 9.0. The classes of compounds attributable to colour

in various process streams exhibit different colour sensitivity according to the pH of

the aqueous media. For example, HMW colourants (e.g., caramels, melanoidins) are

pH insensitive; therefore their colour does not change across pH 4.0–9.0. On the

other hand, flavonoids and phenolic compounds (i.e., colour precursors) are highly pH

sensitive. The colours of these compounds are lightly coloured at pH 4.0 but darken

greatly at pH 9.0 (Smith et al., 1981; Paton, 1992). This is because at pH 9.0, the

ionisation of these compounds is almost complete. Hence, these compounds are more

highly coloured in their anionic form than in their neutral form. As the pH

significantly affects the molecular structure and association-dissociation equilibria of

32

colourant types in sugar process streams, it is possible to determine the different

mechanisms of colour formation taken place during processing by measuring the

indicator value (IV) (Eggleston, 1998). The IV is the ratio of colour at pH 9.0 to that

at pH 4.0 and reflects on the pH sensitivity of the colourants present in sugar process

streams (Godhsall et al., 1991). For example, a decrease in IV value shows a higher

presence of HMW colourants and may be attributable to the Maillard and/or

caramelisation reactions taking place. It is also important to note that lower pH

sensitive compounds (i.e., HMW compounds) will appear to be visually darker than

higher pH sensitive compounds (i.e., LMW compounds). This is due to the higher

absorption of the lower pH sensitive compounds over most of the visible region and

can be avoided if colour is only measured spectrophotometrically at 420 nm (Riffer,

1988). The properties of colourants present in sugar cane juice are summarised in

Table 2.1 (Davis, 2001a).

Table 2.1 Properties of colourants in cane juice (Davis, 2001a).

Monomeric Intermediate Polymeric

Colourant type Flavonoids HADPs Caramels,

Melanoidins

MW (Da) Less than 1,000 1,000–2,500 Greater than 2,500

Ion Neutral at low

pH

Cationic at

pH 1.0–5.0

Anionic at

pH 6.0–14

Cationic at

pH 1.0–5.0

Anionic at

pH 6.0–14

Indicator value 5–40 3–4 1–2

pH sensitivity Sensitive Intermediate Insensitive

Polarity Weak Intermediate High

Smith et al., (1981) further investigated the colour of the juice during the

milling process. In this process, pre-cut sugar cane, also known as billets (ca. 30 cm),

enters a sequence of mills where the sugar cane is crushed and the juice is extracted.

Each mill has a certain number of rollers in which are processed in tandem. The juice

that is extracted as of the crushing and milling of cane from the pinch of the first two

33

rollers (i.e., No. 1 mill) is termed first expressed juice (FEJ). Hence, the remaining

juices extracted from the pinches of the subsequent pairs of rollers are named

according to the order of expressed juice: second expressed juice (2EJ), third

expressed juice (3EJ) and fourth expressed juice (4EJ). Table 2.2 shows an increase

in colour was observed across the milling train. Lower brix content, higher

temperatures and extensive decomposition of fibres in the final mills, increases the

colour of the expressed juice (Curtin and Paton, 1980).

Table 2.2 Colour analyses of milled juice at pH 7.0 (Smith et al., 1981).

Expressed juice Colour at pH 7 (IU)

FEJ 11,100

2EJ 33,000

3EJ 57,100

4EJ 90,800

Eggleston et al., (2003) investigated colour formation across the various stages

of the sugar manufacturing process using three different liming techniques (viz., cold,

intermediate and hot). The results are presented in Figure 2.9. Colour increases after

the liming process due to the reactions of alkaline degradation of reducing sugars.

Paton (1992) and Eggleston et al., (2003) agree that colour decreases during

clarification due to the removal of colourants by the calcium phosphate flocs.

As the brix content increases during the evaporation stage, the juice colour

increases. Several factors such as reaction time, juice pH and sugar concentration

contribute to the increase in colour. Colour formation from the Maillard reaction is

dominant at the earlier stages of evaporation followed by alkaline degradation

reactions (Eggleston, 1998). Based on this colour profile across the sugar

manufacturing stage (Figure 2.9), to produce low coloured sugar, colour removal

strategies should be targeted at mixed juice (MJ) (i.e., combined juice from No. 1 and

No. 2 mills) and/or juice during the evaporating stage. The colourants are partitioned

between the sugar crystals and liquor in the crystallisation step; hence the significant

decrease in colour of the liquor will result in low colour sugar. Table 2.3 shows the

34

extent of colour formed in various sugar process streams in a typical sugar mill and

the partition of colour between liquor and sugar is 6:1 (Smith et al., 1981).

Figure 2.9 Formation of colour among three clarification processes; mixed

juice (MJ), heated juice (HJ), incubated juice (IJ), limed juice (LJ),

flocculated heated limed juice (FHLJ), evaporator supply juice

(ESJ), final evaporator syrup (FES) and raw sugar (RS)

(Eggleston et al., 2003).

Table 2.3 Comparison of colour at pH 7.0 and 9.0 from process streams of a

typical sugar mill (Smith et al., 1981).

Processing stage Colour at pH 7.0 (IU) Colour at pH 9.0 (IU)

FEJ 10,700 23,300

Mixed juice 19,200 37,100

Liquor 15,400 44,500

Magma 27,800 57,400

Massecuite (A-grade) 25,500 63,900

Raw sugar (A-grade) 2,600 5,700

0

2

4

6

8

10

12

14

MJ HJ IJ LJ FHLJ ESJ FES RS

Co

lou

r (

IU)

(x1

03)

Processing Stage of Sample

Hot

Intermediate

Cold

35

2.4.1 Effects of Temperature on Colour Formation

Paton and McCowage (1987) investigated colour formation in factory and

synthetic evaporator supply juice (ESJ) when heated (up to 100 °C) for 5 h. In one of

their studies, the authors compared samples of factory ESJ and deaminated factory

ESJ. The colour in the factory ESJ sample at pH 7.0 increased by about 20% of the

original colour. The colour of the deaminated factory ESJ at pH 7.0 was similar to the

aminated sample despite the latter having 25% less colour than mill ESJ prior to

heating. The authors further investigated this result using a synthetic ESJ consisting

of reducing sugars (equimolar quantities of glucose and fructose), four amino acids

(alanine, aspartic acid, leucine and valine), sucrose and other organic materials

(e.g., dimethyl formamide). The use of synthetic ESJ allowed better understanding on

the mechanisms of colour formation. A summary of their study is as follows:

The formation of colour in the model ESJ after 5 h was of the same order as

the mill ESJ.

The model compounds appeared to have an induction period (0–1.5 h) and a

rapid increase in colour formation was observed over time.

The contribution of caramel colourants was small with or without an amino

acid.

Consistency in the behaviour of amino acid (i.e., did not affect colour greatly)

in both synthetic and factory ESJ samples.

Lower levels of reducing sugars showed slower colour formation.

Lower pH retarded colour formation, however higher pH levels rapidly

produced more colour.

Colour formation was prominent at 100 °C and negligible at 65 °C.

Higher brix content in synthetic ESJ samples resulted in an increase of

melanoidins and a decrease in HADP.

Paton and McCowage (1987) concluded that temperature was the largest

factor that contributed to the formation of colour in ESJ and the formation of colour in

synthetic ESJ (with or without amino acids) was primarily due to HADPs. The rate of

colour formation in synthetic ESJ was slower compared to factory ESJ and this may

be due to the absence of intermediate products of HADPs, MRPs and other impurities

(e.g., phenolic compounds, iron and copper) in the factory ESJ.

36

However, the authors only investigated colour formation for temperatures of

100 ºC and below. The range of interest for temperatures during clarification and

evaporation in a typical sugar mill is considered to be between 65 ºC and 125 ºC. De

Ambrosis (1964) studied the effect of clarified juices at temperatures above 100 °C on

clarified juices. The juices were held in a closed stainless steel vessel (with a mild

steel cap) and heated to the required temperatures.

Comparing the juice colour formation data from De Ambrosis’s (1964) work

and those obtained from Paton and McCowage’s (1987), it can be seen that Paton and

McCowage’s (1987) work showed reduced colour formation rates, while the opposite

was observed in De Ambrosis’s (1964) work. The high colour formation rates in De

Ambrosis’s (1964) work may be due to the vessel’s material of construction

(e.g., iron) having a catalytic effect. Further laboratory analyses on the formation of

colour from three different sugar mills were conducted by Wright and Jegaraj (1992).

The analyses were designed to obtain additional experimental data on colour

formation in juice at higher temperatures (80–125 °C) to complement De Ambrosis’s

(1964) work. The authors examined the colouration rate as functions of temperature,

time, juice (sucrose concentration) and pH, which may impact on the colouring of raw

sugar during the clarification and evaporation stages. An Arrhenius expression for the

rate of colour formation in sugar manufacture was proposed as described in Equation

2.1.

8502

90.8930 10 Ty e

(2.1)

where y is the colour formation rate (% of the initial colour per min)

T is the absolute temperature (K).

Wright and Jegaraj (1992) concluded that the extent of colour in raw sugar can

be minimised by reducing the residence time of process streams at high temperatures

(i.e., lower residence times during clarification, evaporation and crystallisation

processes), lowering the ESJ pH and reducing the content of nitrogenous compounds

(i.e., proteins and amino acids) present in sugar cane plants.

37

2.5 Sugar Decolourisation Technologies

2.5.1 Current Technologies

Methods used to treat sugar process streams to reduce impurity and colour

loadings prior to crystallisation include modified clarification techniques (Eggleston

et al., 2003; Lindeman and O'Shea, 2004); dissolved air floatation (Smith et al., 2000;

Echeverri and Rein, 2006); membrane filtration (Hamachi et al., 2003; Farmani et al.,

2008); chemical precipitation (Moodley, 1993; Doherty et al., 2003); ion exchange

(Broadhurst and Rein, 2003; Bento, 2004); activated carbon adsorption (Mudoga et

al., 2008; Simaratanamongkol and Thiravetyan, 2010) and chemical oxidation via

ozonolysis (Moodley et al., 1999). A summary of the effectiveness of a

decolourisation technique mentioned according to the corresponding types of

colourants, adapted from Davis (2001b) is shown in Table 2.4. The tick symbols ()

represent effective removal of the colourant and cross symbols () for poor colour

removal. Some cells have been left blank due to insufficient data in the literature.

Table 2.4 Decolourisation processes on colourants types existing in juice as

adapted from Davis (2001b).

Fla

von

oid

s

Ph

enoli

cs

Am

ino a

cid

s

Mel

an

oid

ins

Cara

mel

s

HA

DP

s

Defecation

Carbonatation

Sulfitation

Phosphatation

Filtration

Precipitation

Oxidation

Activated carbon

Ion exchange

38

Conventional processes such as defecation, carbonatation, sulfitation and

phosphatation performed during clarification are colour selective and are not so

effective in removing certain types of colourants. To overcome this, combinations of

two or more processes are usually required to produce the best low coloured raw

sugar. Amongst these techniques to produce white or low coloured raw sugar,

activated carbons or ion exchange resins are used. However, there are common

problems with the use of adsorbents and resins such as fouling and exhaustion. These

adsorbents and resins can be regenerated to minimise costs, but there are also

problems associated with the management of waste produced from regeneration.

Therefore to further minimise costs, many sugar manufacturers use SO2 as a pre-

treatment step, thereby reducing the amount of adsorbent or resin used for

decolourisation (Olivério et al., 2007). In some cases, SO2 is further used in syrup

clarification to produce plantation white sugar (Kulkarni, 2010).

2.5.2 Decolourisation using Chemical Additives

Chemical additives in the form of oxidants, precipitants, coagulants and

inhibitors have been used to assist in the colour removal of sugar process streams.

Arguably, SO2 is one of the best performing decolourising agents. The use of SO2 as

a bleaching gas, during sulfitation for plantation white sugar production, is known to

produce very low coloured sugar with a lustre appearance (Saska et al., 2010). It is

widely used in less developed countries but discouraged in developed countries

because of the residual sulfur contamination that is hazardous to human health. In

addition, the low colour in these treated sugars are only temporary, with residual iron

compounds, not removed during the sulfitation process, oxidise and colourise sugar

crystals within a few months of storage (Kulkarni, 2010).

Organic polymers, such as polyacrylamides and polyamines, are commonly

used for coagulation, flocculation and sedimentation processes during the clarification

of juice (Thai et al., 2012). However, the amounts of polymeric material added are

limited due to cost and the possible presence of toxic residual monomers at higher

dosages (Bae et al., 2007); hence these are usually dosed at lower concentrations with

an additional process for optimum colour removal (Moodley, 1993).

39

There has been an increasing trend towards the evaluation of oxidative

chemical additives as an alternative to reduce or inhibit colour formation during sugar

processing. These include the use of chlorinated compounds (Riffer, 1980), ozone

(Moodley et al., 1999), H2O2 (Mane et al., 2000) and Fe(III) in conjunction with

endogenous proteins (Madsen and Day, 2010). The use of chlorinated compounds is

not recommended because of toxicological concerns surrounding the production of

unwanted by-products and residual sulfite/sulfate or chloride/chlorite present in the

final raw sugar product (Davis, 2001a). On the other hand, oxidative decolourants

such as ozone and H2O2 are non-toxic and have shown good decolourisation on sugar

factory process streams.

The reason for the difference between the action of oxidative chemicals and

other colour removal technologies lies in the decolourisation mechanism. Oxidants

destroy colourants rapidly by cleaving unsaturated bonds (i.e., conjugated species),

converting them to non-reactive intermediates which are unable to form colour (Shore

et al., 1984; Riffer, 2000). Examples of these are shown in Schemes 12 and 13 for

ozone and wet peroxide oxidation, respectively (Davis et al., 1998; Neyens and

Baeyens, 2003).

In Scheme 2.12, electrophilic ozone reacts with a nucleophilic alkene (26) to

form an unstable 5-membered ring (i.e., ozonide) (27). The unstable ozonide

decomposes to a carbonyl compound and a zwitterion. Cycloaddition of the two

decomposition products form a stable ozonoide intermediate (28). The ozonoide (28)

is then oxidised in the presence of H2O2 to yield one equivalent of a carboxylic acid

(29) and an aldehyde (30) or ketone.

40

(Alkene)

(26)

R

H R

H

OO

O

OO

O

H

R

R

HO

RH

O

RH

O O

OO

R

H

H

R

R OH

O

R H

O

+

H2O2

(Unstable Ozonide)

(27)

(Carboxylic Acid)

(29)

(Aldehyde)

(30)

(Stable Ozonide)

(28)

Scheme 2.12

Highly reactive •OH radicals produced from the decomposition of H2O2 are

mainly responsible for the oxidation of colourants. Under mild alkaline conditions,

H2O2 dissociates to give water and the perhydroxyl anion (HOO–), a strong

nucleophile (Equation 2.2). The HOO– anion can decompose H2O2 to give water,

oxygen and the •OH radical (Equation 2.3).

H2O2 + OH– H2O + HOO– (2.2)

HOO– + H2O2 H2O + O2 + •OH (2.3)

In Scheme 2.13, hydroxyl radicals generated from the decomposition of H2O2

attach to the aromatic ring of benzene (31) causing the ring to open and yield muconic

acid (32). The product (32) can possibly undergo further oxidative degradation to

produce harmless reaction products such as CO2 and water.

41

H OH H OHOH

O

OH

O

OH

Benzene Muconoic acid

OH OH OH

(31) (32)

Scheme 2.13

Studies showing the destruction of flavonoids and colour precursors in sugar

cane juice using H2O2 as the oxidant have been conducted previously (Mane et al.,

1992; Mane et al., 1998; Mane et al., 2000; Saska, 2007). Saska (2007) reported up

to 30% reduction in colour by using H2O2 on Colombian plantation white sugar at

dosages of 100–500 ppm, which is also in agreement with the findings from Mane et

al., (2000). Also, in their most recent study, Mane et al., (2000) reported more than

20% reduction in both colour precursor compounds and SO2 content in Indian

plantation white sugars. They observed a decrease in phenolic content (40–50%) in

H2O2 treated raw syrup, therefore minimising the chance for these compounds to take

part in further colour forming reactions. There were also reductions in raw sugar

colour (12–35%), amino acids (15–25%) and starch (12–13%). Furthermore, the

authors reported lower colour development upon stored raw sugars treated directly

with H2O2.

Ozone, on the other hand, is a stronger oxidant than H2O2. A decrease by

about one third of the initial syrup colour was achieved with 250 ppm ozone (Davis et

al., 1998). However, unlike H2O2, ozone is very expensive to produce and is not cost

effective for juice or syrup decolourisation (Moodley et al., 1999). Therefore, a

technology based on H2O2 has the potential to produce low coloured raw sugar at a

reasonable cost.

42

2.5.3 Novel and Potential Technologies

Advanced Oxidation Processes (AOPs)

In recent years, AOPs have become increasingly attractive to treat a wide

range of azo dyes (Joseph et al., 2000), contaminated soils (Kong et al., 1998),

polluted oceans and streams (Trovó et al., 2009) and industrial wastewaters (Lucas

and Peres, 2009). These processes involve the in situ generation of highly reactive

•OH radicals by chemical (e.g., acids, inorganic salts), photocatalytic (e.g., solar,

ultraviolet (UV) light), electrochemical (e.g., cathode electrodes), radiolytic

(γ–radiolysis) and physical (e.g., ultrasound) methods. The oxidation potential of

•OH radicals is stronger (2.80 V) than ozone (2.07 V) and H2O2 (1.80 V) and can

completely degrade and mineralise organic compounds and impurities.

Fenton Oxidation Process

An example of an established and commercialised AOP is the catalytic

activation of H2O2 using Fe(II), typically referred to as the Fenton oxidation process.

The conventional homogenous Fenton oxidation process is already in use for the

treatment of industrial wastewaters (Guedes et al., 2003; Cañizares et al., 2007; Lu et

al., 2009). It is an attractive process for its low capital costs, low toxicity of reagents

and ease of application. The Fenton process involves the production of •OH radicals

through the homogenous catalytic decomposition of H2O2 using Fe(II). The generally

accepted free radical chain mechanism for the oxidation of organic compounds (RH)

via the Fenton process is shown in Equations 2.4–2.10 (Walling, 1975; Pignatello,

1992; Kang and Hwang, 2000; Sun et al., 2007).

43

Fe2+ + H2O2 Fe3+ + OH– + •OH (2.4)

RH + •OH R• + H2O (2.5)

H2O2 + •OH H2O + •O2H (2.6)

Fe2+ + •OH Fe3+ + OH– (2.7)

•OH + •OH H2O2 (2.8)

H2O2 + Fe3+ Fe2+ + H+ + •O2H (2.9)

Fe3+ + HO2• Fe2+ + H+ O2 (2.10)

As depicted in Equation 2.5, a radical chain oxidation reaction is initiated

through the formation of organic radicals (R•) by hydrogen atom abstraction, electron

transfer or electrophilic addition (Neyens and Baeyens, 2003; Pignatello et al., 2006).

These organic radicals are highly reactive, which form peroxyl radicals (Equation

2.11) (Lipczynska-Kochany et al., 1995), and further oxidation through the addition

of •OH or molecular oxygen, in turn would oxidise and mineralise to harmless

products such as CO2 and H2O (Equation 2.12) (Sun et al., 2009; Oturan et al., 2011).

R• + O2 ROO• (2.11)

ROO• + •OH/O2 CO2 + H2O (2.12)

The two important factors to consider in the Fenton process are the dosage

levels of H2O2 and Fe(II) (Chamarro et al., 2001). The H2O2 dose improves the

decolourisation whilst the reaction kinetics is dependent on the amount of Fe(II)

added.

In the last few years, much attention has been paid to the variations and

development of advanced Fenton processes to improve the oxidation performance and

alleviate one of the major drawbacks of the Fenton process, which is the production of

iron sludge. These include photo-Fenton (e.g., solar and UV light) (Kuo et al., 2012;

Lucas et al., 2012), electro-Fenton (Wang et al., 2012), sono-Fenton (Babuponnusami

and Muthukumar, 2011), Fenton-like (e.g., Fe(III), chelated iron) (Li et al., 2007;

Nichela et al., 2010) and heterogeneous Fenton (e.g., Fe-pillared clays, zero valent

iron) (Catrinescu et al., 2012; Segura et al., 2012). However, most of these

technologies have not yet been commercialised. Therefore, the conventional Fenton

process, which is simple and requires no specialised equipment, is still the only cost

effective process to treat a wide range of compounds and convert them into less

44

harmful compounds that are easier to be removed through other purification

techniques (i.e., filtration, coagulation, ion exchange) (Üstün et al., 2007; Arsene et

al., 2011; Elías-Maxil et al., 2011).

In previous studies, Fenton oxidation has been employed to target individual

model phenolic acids in synthetic industrial process streams. Rivas et al. (2001)

evaluated the degradation of p–hydroxybenzoic acid (10 mM) and found under

optimum conditions of 5.0 mM Fe(II), 2.7 M H2O2 and pH 3.2; 95% of the phenolic

acid was degraded after 30 min at 20 °C. In a later study, Rivas et al. (2005) reported

98% removal of protocatechuic acid (0.65 mM), under similar operating conditions to

those of p–hydroxybenzoic acid. Benitez et al. (2005) reported 79% degradation of

gallic acid (0.59 mM) after 40 min at 25 °C using 2.5 × 10–2 mM Fe(II), 2.5 mM H2O2

and at pH 3.0.

Even though, the Fenton process or AOPs in general are non-selective

processes, the degradation efficiency of phenolic compounds via •OH radical attack

differs from one type to another, as it depends on many factors including number of

substituents (e.g., hydroxyl and methoxyl groups) attached on the aromatic ring; •OH

radical positioning and bonding sites on the aromatic ring; and preference for •OH

radical attack on additional functional groups (e.g., vinyl groups) than the aromatic

ring (Rice-Evans et al., 1996; Sroka and Cisowski, 2003). Also, the degradation of

phenolic compounds in mixtures is expected to be different due to competing

reactions between the phenolics, the •OH radicals and the intermediates formed

during the course of the oxidation reaction.

The degradation of mixtures of phenolic compounds has been studied using

Fenton oxidation (Heredia et al., 2001), Fenton-like oxidation (Du et al., 2006), ozone

(Amat et al., 2003) and other AOPs, mainly photocatalysis processes (Gernjak et al.,

2003; Kusvuran et al., 2005; Azabou et al., 2007; Monteagudo et al., 2011). Heredia

et al. (2001) developed a kinetic model for the oxidation of phenolic compounds

(viz., caffeic, p–coumaric and ferulic acids) by the Fenton process. These compounds

are the main colour precursors present in sugar cane juice and are known to

participate in reactions producing colour that results in the final raw sugar product.

The rate constants for the degradation of the individual phenolic acids in a mixture of

acids, were deduced from the developed model and it was found that the degradation

45

process at a constant Fe(II) concentration at 30 °C proceeded in the following order;

ferulic acid > p–coumaric acid > caffeic acid. No reason was given for the

differences in the rate of degradation among these phenolic acid mixtures. The

ozonisation of solutions containing a mixture of cinnamic, caffeic, p–coumaric and

ferulic acids was studied by Amat et al. (2003). These workers found that the

behaviour of caffeic acid though followed a similar mechanism as that of cinnamic

acid had a different reaction rate due to a greater number of intermediates. None of

these studies optimised the degradation process of the individual acids within a

mixture of phenolic acids by the Fenton process, nor examined the interactive effects

of various operating parameters on the degradation of each acid. Also, none of the

aforementioned studies were conducted in sucrose solutions, and the reaction times

were generally an order of magnitude higher than that required in the various stages of

the sugar manufacturing process. The role of sucrose (apart from its free radical

scavenging ability) in the degradation process of these acids in a mixture has not been

reported (Morelli et al., 2003). As such, the focus in this present study was to provide

further insight into the degradation of these phenolic acids by the Fenton process.

The decolourisation of a baker’s yeast waste product, which primarily consists

of cane molasses, using Fenton oxidation was investigated by Pala and Erden (2005).

The colourants present in molasses include caramels, melanoidins, colour precursors,

iron-phenol complexes and some cane pigments. Neyens and Baeyens (2003) stated

that acidic pH levels (about pH 3.0) are usually optimum for Fenton oxidation.

However, Pala and Erden (2005) established the optimum pH for colour removal via

the Fenton process was at pH 4.0. The best colour removal efficiency of 99% at

25 °C and pH 4.0 was achieved with dosages of 22 mM Fe(II) and 24 mM H2O2 for

20 min. Under the optimum pH and treatment times, dosages of 11 mM Fe(II) and

18 mM H2O2 were enough to remove colour with an efficiency of 97%.

Madsen and Day (2010), though not using the Fenton oxidation process,

demonstrated the removal of phenolic and other colourants from raw juice using

endogenous proteins as well as Fe(III) as an oxidative catalyst. The treatment

produced clarified juice with up to 70% lower colour in cold liming clarification

(i.e., addition of lime before juice incubation) than juice produced by hot liming

clarification. However, clarification via cold liming results in less precipitation of

calcium phosphate precipitates and impurities due to the higher solubility of calcium

46

ions at lower temperatures (35–40 °C) (Doherty et al., 2002). This would result in

more turbid clarified juices and higher sucrose losses. Hence, the presence of high

calcium levels in clarified juice will heavily impact on the downstream processes,

particularly during evaporation where the formation of scale takes place in the

evaporators.

On the basis of the information obtained from the literature, the aim of this

present project was to develop, optimise and evaluate the Fenton oxidation process for

the degradation and decolourisation of selected colour precursors and colourants

present in sugar process streams.

47

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human health. Biotechnology Journal, 2(10), 1214-1234.

Walling, C. (1975). Fenton's reagent revisited. Accounts of Chemical Research, 8,

125-131.

Wang, Y., Li, X., Zhen, L., Zhang, H., Zhang, Y., & Wang, C. (2012). Electro-Fenton

treatment of concentrates generated in nanofiltration of biologically pretreated

landfill leachate. Journal of Hazardous Materials, 229–230, 115-121.

Wiggins, L. F. (1953). The Nitrogen-Containing Nonsugars (The Amino Acids and

Proteins). In P. Honig (Ed.), Principles of Sugar Technology. Amsterdam:

Elsevier.

56

Wright, P. G., & Jegaraj, T. J. (1992). A study of colour formation in the processing

of cane juice, Sugar Research and Development Corporation.

Yang, B. Y., & Montgomery, R. (1996). Alkaline degradation of fructofuranosides.

Carbohydrate Research, 280, 47-57.

57

CHAPTER 3

Determination of Phenolic Compounds

in Factory Sugar Cane Juices

3.1 Introduction..................................................................................... 58

3.2 Materials and Methods................................................................... 58

3.2.1 Reagents and Solvents......................................................... 58

3.2.2 Specification of Samples...................................................... 59

3.2.3 Sample Preparation............................................................. 60

3.2.4 Instrumental Procedures and Analysis................................ 60


Measurements......................................................................

62

3.3 Results and Discussion.................................................................... 62

3.3.1 Colour Analyses of Juices................................................... 62

3.3.2 Phenolic Content in Juices.................................................. 63

3.4 Summary.......................................................................................... 70

58

3.1 Introduction

A new approach that has the potential for efficient and cost-effective

decolourisation of sugar process streams during the manufacture of raw sugar is

through the use of the Fenton oxidation process. As a first step towards developing

this technology, the colour and composition of phenolic acids (i.e., colour precursors)

present in sugar cane juices obtained from three different harvesting methods were

determined. These methods include burnt cane harvesting with all trash (i.e., tops and

leaves) extracted; green cane harvesting with a proportion of the trash extracted and

whole crop cane harvesting with no trash extracted.

The colour content of each juice sample at pH 7.0 was measured

spectrophotometrically at a wavelength of 420 nm according to the international

(ICUMSA) method. The phenolic content in the juices was determined using

reversed-phase high-performance liquid chromatography (HPLC). Juice samples

were hydrolysed based in the standard method and extracted based on the procedures

reported by Paton (1978) and Schieber et al. (2001) respectively, prior to HPLC

analysis. This procedure was modified by changing the sample preparation method

and HPLC operating conditions in order to improve the response of the phenolic

compounds for accurate quantification. The most concentrated phenolic compounds

were selected for oxidative degradation by the Fenton process in subsequent chapters.

3.2 Materials and Methods

3.2.1 Reagents and Solvents

All chemicals purchased were of analytical reagent (AR) grade and used as

supplied without further purification. Solvents for chromatographic analyses were of

super gradient HPLC grade from Scharlau (Sentmenat, Spain). Solutions were

prepared using ultrapure (Milli-Q) water from a Millipore system (Bedford, MA,

USA) with a resistivity of 18.2 MΩ.cm.

Caffeic acid, (±)–catechin, chlorogenic acid, chrysin, m–coumaric acid,

o–coumaric acid, p–coumaric acid, coumarin, 2,3–dihydroxybenzoic acid,

5,7–dihydroxy–4–methoxyisoflavone, diosmin, ferulic acid, gallic acid, hesperidin,

59

hesperetin, HMF, homogentisic acid, 4–hydroxybenzoic acid, kojic acid, morin,

quercetin, α–resorcylic acid, β–resorcylic acid, rutin, syringaldehyde,

3,4,5–trimethoxybenzoic acid and vanillic acid were purchased from Sigma-Aldrich

(St. Louis, MO, USA). Ammonium chloride, ammonium hydroxide, glacial acetic

acid, lead acetate and sodium hydroxide were obtained from Ajax Finechem (Seven

Hills, NSW, Australia). Methyl orange indicator and vanillin were supplied from

Merck (Darmstadt, Germany) and furfural was from Fluka (Buchs, Switzerland).

Protocatechuic acid and sinapinic acid were purchased from Acros Organics (Geel,

Belgium). Celite 577 (diatomaceous earth) was obtained from World Minerals (Santa

Barbara, CA, USA).

3.2.2 Specification of Samples

First expressed juice from burnt harvested sugar cane was obtained from the

processing lines at Condong Sugar Mill (Condong, NSW, Australia). Whole crop

harvested cane FEJ was obtained by harvesting sugar cane located around Condong

Sugar Mill in the field and expressing the juice with a laboratory hammer mill

designed by the Sugar Research Institute (SRI) (Brisbane, QLD, Australia). The

specification of the mill was as follows: 430 × 220 mm roll; 12.8 mm groove pitch;

12.0 mm groove depth; 4 rpm operating speed; and a 10 hp powered motor running at

7.5 kW. The juice was collected by pressing through a 1 mm mesh sieve.

Both FEJs were obtained during the crushing season in 2009. Samples of

primary juice (PJ) (i.e., incubated MJ prior to lime addition) from burnt cane and

green cane were obtained at ca. 76 °C and pH 5.15 from Condong Sugar Mill during

the crushing season in 2010. All juices were stored at –22 °C. In total, four juices (2

× FEJs and 2 × PJs) were analysed. The following analyses of the four juice samples

are unrelated and not comparable. The results obtained provide an insight on the

levels of colour and phenolic content present in each juice type.

60

3.2.3 Sample Preparation

The mill juices were analysed as phenolic extracts after alkaline hydrolysis.

Hydrolysis was carried out under ambient temperature using 2.0 M NaOH on

centrifuged juice (50% (v/v)) for 30 min at ambient temperature with magnetic

stirring (280 rpm).

Two different liquid-liquid extraction procedures were carried out for the

determination of phenolic acid content in the juices collected as follows:

Method A. The hydrolysed juice was neutralised by adjusting the pH to 3.0

with 6.0 M HCl and extracted three times with diethyl ether (20 mL). The combined

extracts were dried over anhydrous sodium sulfate followed by evaporation to dryness

in vacuo to constant weight. The individual residues were weighed, dissolved in

water (10 mL) and membrane filtered (0.45 µm) prior to analysis by HPLC. The

procedure is that developed by Paton (1978).

Method B. Hydrolysed juices were treated in the same manner as described in

Method A but instead were extracted three times with ethyl acetate (50 mL). The

dried individual residues were dissolved in HPLC grade methanol (10 mL) prior to

membrane filtration followed by HPLC analysis.

3.2.4 Instrumental Procedures and Analyses

The organic extracts were analysed using reversed-phase HPLC with

UV/Visible (UV/Vis) diode-array detection (DAD). The method was adapted from a

previously reported method for the determination of phenolic acids in apple and pear

juices (Schieber et al., 2001). Analyses were performed on a Hewlett Packard

HP/Agilent 1100 Series HPLC system (G1379A micro-degasser, Japan; G1311A

quaternary pump, Germany; G1313A automatic liquid sampler (ALS), Germany;

G1315B diode-array detector, Germany) using a Waters Symmetry C18 column

(150 × 3.9 mm i.d.) with a Waters Guard-Pak guard holder containing a Waters

Guard-Pak Resolve C18 guard insert (10 µm) (Milford, MA, USA). The mobile

phase consisted of 2.0% (v/v) glacial acetic acid in water (as eluent A) and methanol

61

(as eluent B). The gradient programs for extracts produced from each method were as

follows:

Method A. 10% B to 17% B (18 min), 17% B to 23% B (12 min), isocratic

(10 min), 23% B to 31% B (13 min), 31% B to 46% B (12 min), 46% B to 55% B

(5 min), 55% B to 100% B (5 min), isocratic (8 min), 100% B to 10% B (2 min) and

isocratic (5 min).

Method B. 2% B to 5% B (10 min), 5% B to 20% B (50 min), 20% B to 50%

B (20 min), isocratic (5 min) and 50% B to 2% B (5 min).

Simultaneous detection at specific wavelengths (280 nm, 320 nm, 370 nm and

420 nm) was subtracted against a reference wavelength (600 nm). The wavelengths

were chosen for identification and quantification of the various types of phenolic

compounds. Data on hydroxybenzoic acids can be collected at 280 nm;

hydroxycinnamic acids at 320 nm; flavanols and chalcones at 370 nm; and other

flavonoid derivatives at 420 nm (Cai et al., 2004; Stalikas, 2007).

Aliquots of samples were membrane filtered (0.45 μm) prior to injection into

the HPLC system. Injection volumes for all samples were 10 μL and 8.0 μL for

extracts produced from Methods A and B respectively. Column temperature was

25 °C; flow rate was 1.0 mL and run time was 90 min. After each run, the

chromatographic system was equilibrated for 10 min. Data acquisition was performed

using the Agilent ChemStation (Rev. A.09.03) software package. Analyses of

samples were carried out in triplicate.

Identification of peaks was based on the conformance of UV/Vis spectra and

retention times with the corresponding authentic standards. Calibration curves for 18

compounds were constructed using five different standard concentrations over the

concentration ranges expected in sugar process streams (Curtin and Paton, 1980;

Payet et al., 2006). The calibration curves were linear (R2 = 1.00). The peak heights

of the target compounds were within the linear range of the calibration curve.

Analyses of standards were carried out in triplicate.

62

3.2.5 Colour, Refractive Index and Total Soluble Solids Measurements

Celite 577 (7.5 g) was suspended in 50 mL of juice and stirred magnetically

for 10 min at ambient temperature. The adsorbed fine particles present in the

suspension were removed by vacuum filtration. The filtrate was diluted to an

appropriate absorbance range and membrane filtered (0.45 μm) before adjusting the

pH to 4.0 using 0.01 M HCl, and pH 7.0 and pH 9.0 using 0.01 M NaOH.

Absorbance measurements were conducted spectrophotometrically at 420 nm (A420)

on a GBC Scientific Cintra 40 double-beam UV/Vis spectrophotometer (Braeside,

VIC, Australia) using cells of 1.0 cm path length. Data acquisition was performed

using the GBC Spectral 1.50 software package. The resulting colour of each sample

was calculated as:

4201000Colour (IU)

Cell Length Sucrose Concentration

A

(3.1)

The total soluble solids (TSS) of the juice and refractive index (RI) of the

filtrate were measured at ambient temperature using a Bellingham and Stanley RFM

342 refractometer (Tunbridge Wells, UK) accurate to ± 0.01 °Bx and ± 0.00001 RI

units respectively. The RI values were used to determine the corresponding

concentration of sucrose in solution (g/mL) based on Table XII in the Bureau of Sugar

Experiment Stations (BSES) Laboratory Manual for Australian Sugar Mills (BSES,

2001).

3.3 Results and Discussion

3.3.1 Colour Analyses of Juices

Colour is conventionally measured at pH 7.0. Flavonoids and phenolic

compounds are pH sensitive and their colour profile increases greatly from minimal

colour in untreated MJ and FEJ (at pH 4.0–5.0) up to near-maximum colour at pH 9.0

(Paton, 1992). Therefore, colour measured at pH of 7.0 or higher would provide

satisfactory measurement of the presence of flavonoids and phenolic compounds. The

colour of Condong Sugar Mill juices is presented in Table 3.1. High colour and

impurity was recorded with juices expressed from whole crop harvested cane. This is

63

primarily due to green cane harvesting where green tops and brown leafy trash are

processed (Eggleston et al., 2010).

Table 3.1 Colour of factory sugar cane juices recorded at pH 7.

PJ FEJ

Green cane Burnt cane Whole crop Burnt cane

TSS (°Bx) 13.19 14.97 16.39 19.43

RI 1.3537 1.3601 1.3579 1.3690

Colour (IU) 20,000 12,700 11,400 10,400

*Mean values (n = 3). % Relative standard deviation (RSD) was ≤ 0.7%.

3.3.2 Phenolic Content in Juices

The phenolic compounds separated from the cane juice extracts using Method

A are shown in Figure 3.1. Baseline separation was achieved for all identified

components. The m– and o–isomers of coumaric acid were not detected in any of the

extracts analysed. The elution order of the phenolic compounds was consistent with

previous studies under different chromatographic conditions with the exception of

2,3–dihydroxybenzoic acid and chlorogenic acid (Curtin and Paton, 1980).

64

Figure 3.1 Separation of a typical mixture of compounds in the FEJ extract of

burnt harvested cane by HPLC-DAD (Method A, UV/Vis detection

at 280 nm). 1 = gallic acid (tentative), 2 = HMF,

3 = 4–hydroxybenzoic acid, 4 = chlorogenic acid, 5 = vanillic acid,

6 = caffeic acid, 7 = 2,3–dihydroxybenozic acid, 8 = protocatechuic

acid (tentative), 9 = p–coumaric acid, 10 = ferulic acid.

The concentrations of each compound varied with the juice type. These are

tabulated in terms of mM on dry content as shown in Table 3.2. The concentrations

of phenolic compounds in whole crop and green harvested cane juices are

substantially lower than burnt harvested cane juices. This is probably due to the

valorisation of lignin, a component of bagasse, during cane burning.

Table 3.2 shows that higher amounts of HMF were identified in both FEJ and

PJ extracts of burnt cane compared to the extracts of green cane and whole crop. This

is due to the dehydration of sugars, particularly reducing sugars, to HMF as a result of

high temperatures generated during the burning of cane prior to harvesting

(Huber et al., 2006). Prior to this work, the quantification of HMF in Australian FEJ

and PJ extracts using this method has not been reported in the literature. Also, the

concentration of caffeic acid (Table 3.2) was relatively lower than other phenolic

acids in comparison to previous work on Australian factory cane juice (Curtin and

Paton, 1980).

-10

0

10

20

30

40

50

0 5 10 15 20 25 30 35

Ab

sorb

na

ce (

mA

U)

Retention Time (min)

34

5 6

7

8 9

10

1

2

65


sugar cane juices using Method A.*

PJ FEJ


Caffeic 10 20 0.68 6.9

Chlorogenic 2.2 19 0.20 8.6

p–Coumaric 14 87 1.2 19

2,3–Dihydroxybenzoic 12 24 0.80 7.7

Ferulic 6.0 11 0.48 5.0

4–Hydroxybenzoic 8.3 20 0.45 5.0

Vanillic 14 25 0.68 7.3

HMF 0.43 7.8 0.40 1.4

*Mean values (n = 3). % RSD was < 5.0%.

Higher concentrations of phenolic compounds are present in PJs compared to

FEJs (Table 3.2). This is probably due to the decomposition of flavonoids followed

by oxidation of the intermediate products and further degradation of lignin products at

the relatively higher processing temperatures of PJ.

Table 3.3 shows a comparison of the phenolic acid and HMF composition

based on the PJs from Table 3.2 in terms of mM on juice, to those reported by Curtin

and Paton (1980). The total amount of phenolic compounds are considerably higher

than those previously reported by Curtin and Paton (1980). The differences between

the two sets of data may be related to differences in the cane varieties or the

differences in the analytical procedures used for phenolic composition analysis.

Evident from Table 3.1, the juices expressed from green cane and whole crop

contain higher colour but lower amounts of phenolic compounds than the juices

expressed from burnt cane (Table 3.2). It is therefore deduced that the juices

expressed from green cane and whole crop cane harvesting contain a higher

proportion of cane pigments (e.g., flavonoids).

66

Table 3.3 Phenolic acids (mM on juice) and HMF by HPLC-DAD of PJs

using Method A.*

Green cane Burnt cane Burnt cane†

Caffeic 0.044 0.055 0.083

Chlorogenic 0.009 0.051 0.000

p–Coumaric 0.060 0.240 0.002

2,3–Dihydroxybenzoic 0.053 0.065 0.001

Ferulic 0.026 0.028 0.002

4–Hydroxybenzoic 0.036 0.055 0.001

Vanillic 0.058 0.067 0.002


†Cane juice data based from Curtin and Paton (1980).

The reversed-phase HPLC method (Method B) was then optimised for

separation and identification of phenolic compounds. Different solvents for the

liquid-liquid extraction of phenolics and dissolution of the dried residues as well as

modifications of the gradient program were conducted to obtain chromatograms with

good resolution of peaks with an acceptable analysis time. Changes in the injection

volume and gradient program significantly affected the resolution and separation of

peaks.

Ethyl acetate was chosen as the solvent for liquid-liquid extraction in place of

diethyl ether as it has a better extraction efficiency and higher recovery yields for

phenolic acids but not phenolic aldehydes (Simón et al., 1990). The improved

solubility of the dried residues in methanol resulted in better detector response

(Robbins, 2003). The separation of a standard mixture of 20 phenolic compounds

(viz., hydroxybenzoic and hydroxycinnamic acids), HMF and furfural monitored at

280 nm using Method B is shown in Figure 3.2.

67

Figure 3.2 Separation of a standard mixture of compounds by HPLC-DAD

(Method B, UV/Vis detection at 280 nm). 1 = gallic acid, 2 = HMF,

3 = protocatechuic acid, 4 = furfural, 5 = 4–hydroxybenzoic acid,

6 = (±)–catechin, 7 = vanillic acid, 8 = caffeic acid, 9 = chlorogenic

acid, 10 = vanillin, 11 = p–coumaric acid, 12 = syringaldehyde,

13 = ferulic acid, 14 = sinapinic acid, 15 = coumarin,

16 = o–coumaric acid, 17 = 3,4,5–trimethoxybenzoic acid,

18 = rutin, 19 = diosmin, 20 = chrysin, 21 = morin,

22 = quercetin.

As shown in Figure 3.2 the baseline separation was achieved for virtually all

components evenly across the whole chromatogram except for chlorogenic acid and

sinapinic acid which are overlapped by caffeic acid and coumarin respectively. For

these four compounds, chlorogenic acid was not quantified as it was superimposed by

caffeic acid.

The HPLC chromatogram of burnt cane PJ using Method B is shown in Figure

3.3. The concentration of phenolic components of the cane juice extracts are shown

in Table 3.4. The phenolic acid content detected in the sugar cane extracts in this

study were similar to the values obtained by Payet et al., (2006) for various sugar

process streams and products after juice clarification. The concentrations of the

phenolic compounds detected are consistent to those found in orange juice (Rapisarda

-10

0

10

20

30

40

50

60

0 10 20 30 40 50 60 70 80 90

Ab

sorb

an

ce (

mA

U)


1

2

3

4

5

67

8

9

1011

12 1314

1516

17 1819 20

2122

68

et al., 1998), mandarin juice (Kelebek and Selli, 2011) and commercial fruit juices

(viz., apple, grape strawberry) (Díaz-García et al., 2013).

As shown in Figure 3.3 and Table 3.4, the juice extracts mainly consisted of

hydroxybenzoic and hydroxycinnamic acids, with the latter having the highest total

concentration. Caffeic, p–coumaric and ferulic acids are the most concentrated

hydroxycinnamic acids, while 4–hydroxybenzoic and vanillic acids are the most

concentrated hydroxybenzoic acids.

Figure 3.3 Separation of a typical mixture of compounds in the PJ extract of

burnt harvested cane by HPLC-DAD (Method B, UV/Vis detection

at 280 nm). 1 = gallic acid, 2 = HMF, 3 = protocatechuic acid,

4 = furfural, 5 = 4–hydroxybenzoic acid, 6 = vanillic acid,

7 = caffeic acid, 8 = p–coumaric acid, 9 = syringaldehyde,

10 = ferulic acid, 11 = sinapinic acid, 12 = coumarin, 13 = rutin,

14 = diosmin, 15 = chrysin, 16 = morin, 17 = quercetin.

-10

0

10

20

30

40

50

60

0 10 20 30 40 50 60 70 80 90

Ab

sorb

an

ce (

mA

U)


1112

1

23

4

56

7

8

9

10

1314

15 16 17

69


sugar cane juices using Method B.*

PJ FEJ


Hydroxybenzoic acids

2,3–Dihydroxybenzoic 20 17 13 25

Gallic 22 8.1 0.80 29

4–Hydroxybenzoic 33 25 24 43

Protocatechuic 12 6.0 6.9 11

Vanillic 54 34 34 82

Hydroxycinnamic acids

Caffeic 53 36 6.8 190

p–Coumaric 140 120 66 160

Ferulic 180 100 62 190

Sinapinic 6.8 4.7 4.0 15

Flavonoids

Chrysin 12 1.9 2.2 –

Morin 8.4 7.7 7.5 15

Quercetin 16 7.5 10 16

Rutin 5.1 2.7 2.5 5

Other phenolic compounds

Coumarin 7.7 11 6.5 27

Syringaldehyde 18 15 11 24

Non-phenolic compounds

Furfural 6.0 3.4 – 7.7

HMF 0.83 0.12 – 5.7

* Mean values (n = 3). % RSD was ≤ 13.4%.

The flavonoid compounds, chrysin, diosmin, morin, quercetin and rutin were

also detected using Method B. These compounds were eluted towards the end of the

chromatogram (> 60 min) as shown in Figure 3.3. It is assumed that the unidentified

peaks within the 60–90 min timeframe of each chromatogram are attributable to

70

flavonoid compounds. The m– and o– isomers of coumaric acid were not found. The

components (±)–catechin, 5,7–dihydroxy–4–methoxyisoflavone, hesperidin,

hesperetin, homogentisic acid, kojic acid, α–resorcylic acid, β–resorcylic acid and

3,4,5–trimethoxybenzoic acid were also not identified in any of the four juice extracts.

These compounds are typically found in commercial products such as honeys

(Gómez-Caravaca et al., 2006) and fruit juices (Díaz-García et al., 2013).

3.4 Summary

Fifteen phenolic compounds, HMF and furfural were quantified in juice

extracts of FEJ and PJ process streams expressed from burnt, green and whole crop

harvested cane. The results show that juice expressed from whole crop cane has

significantly higher colour but lower concentrations of phenolic acids than juices

expressed from burnt cane. It was deduced that the juices expressed from green cane

and whole crop cane harvesting contain a higher proportion of cane pigments.

Changes to the extraction procedure, sample preparation and chromatographic

conditions as outlined in the modified method (Method B), gave more definitive peak

separation and showed an overall improved response to phenolic acids and revealed

the presence of flavonoid compounds. Interestingly, the concentrations of phenolic

acids separated using Method A showed a higher proportion of hydroxybenzoic acids

than hydroxycinnamic acids, possibly due to the solubility effect of the dried extracts.

However, the opposite was observed when the extracts were separated using Method

B. Using the modified method, the HPLC results reveal that caffeic, p–coumaric and

ferulic acids were the three main phenolic acids present in FEJ and PJ extracts

sourced from burnt cane, green cane and/or whole crop harvested cane.

71

References

BSES (2001). Table XII, Laboratory Manual for Australian Sugar Mills (Vol. 2, pp.

1-7). Indooroopilly, QLD, Australia: Bureau of Sugar Experiment Stations.

Cai, Y., Luo, Q., Sun, M., & Corke, H. (2004). Antioxidant activity and phenolic

compounds of 112 traditional Chinese medicinal plants associated with

anticancer. Life Sciences, 74(17), 2157-2184.




Díaz-García, M. C., Obón, J. M., Castellar, M. R., Collado, J., & Alacid, M. (2013).

Quantification by UHPLC of total individual polyphenols in fruit juices. Food

Chemistry, 138(2–3), 938-949.

Eggleston, G., Grisham, M., & Antoine, A. (2010). Clarification properties of trash

and stalk tissues from sugar cane. Journal of Agricultural and Food

Chemistry, 58(1), 366-373.

Gómez-Caravaca, A. M., Gómez-Romero, M., Arráez-Román, D., Segura-Carretero,

A., & Fernández-Gutiérrez, A. (2006). Advances in the analysis of phenolic

compounds in products derived from bees. Journal of Pharmaceutical and

Biomedical Analysis, 41(4), 1220-1234.

Huber, G. W., Iborra, S., & Corma, A. (2006). Synthesis of transportation fuels from

biomass: chemistry, catalysts and engineering. Chemical Reviews, 106(9),

4044-4098.

Kelebek, H., & Selli, S. (2011). Identification of phenolic compositions and the

antioxidant capacity of mandarin juices and wines. Journal of Food Science

and Technology, 1-8.

Paton, N. H. (1978). A method for the separation and identification of phenolic acids

in sugar products. Proceedings of the International Society of Sugar Cane




Payet, B., Shum Cheong Sing, A., & Smadja, J. (2006). Comparison of the

concentrations of phenolic constituents in cane sugar manufacturing products

with their antioxidant activities. Journal of Agricultural and Food Chemistry,

54, 7270-7276.

Rapisarda, P., Carollo, G., Fallico, B., Tomaselli, F., & Maccarone, E. (1998).

Hydroxycinnamic acids as markers of Italian blood orange juices. Journal of


72

Robbins, R. J. (2003). Phenolic acids in foods: an overview of analytical

methodology. Journal of Agricultural and Food Chemistry, 51(10), 2866-

2887.

Schieber, A., Keller, P., & Carle, R. (2001). Determination of phenolic acids and

flavonoids of apple and pear by high-performance liquid chromatography.

Journal of Chromatography A, 910, 265-273.

Simón, B. F., Pérez-Ilzarbe, J., Hernández, T., Gómez-Cordovés, C., & Estrella, I.

(1990). HPLC study of the efficiency of extraction of phenolic compounds.

Chromatographia, 30(1-2), 35-37.

Stalikas, C. D. (2007). Extraction, separation, and detection methods for phenolic

acids and flavonoids. Journal of Separation Science, 30, 3268-3295.

73

CHAPTER 4

Degradation of

Hydroxycinnamic Acids

4.1 Introduction..................................................................................... 74



4.2.2 Catalytic and Non-catalytic Oxidation of Caffeic Acid....... 75

4.2.3 Fenton Oxidation Reactions for Caffeic Acid Degradation 76


Hydroxycinnamic Acid Mixtures.........................................

78

4.2.5 Instrumental Procedures and Analyses............................... 78

4.2.6 Performance Assessment of the Fenton Oxidation Process 79

4.2.7 Design of Experiments......................................................... 80

4.2.8 Statistical Analysis.............................................................. 82

4.2.9 Evaluation of the Interactions between Fe(II) and

Hydroxycinnamic Acids.......................................................

82


4.3.1 Catalytic and Non-catalytic Oxidation of Caffeic Acid in

Aqueous Systems..................................................................

83

4.3.2 Optimisation of Process Parameters for the Degradation

of Caffeic Acid in Sugar Solutions......................................

87

4.3.3 Degradation of Hydroxycinnamic Acid Mixtures............... 100

4.4 Summary.......................................................................................... 130

74

4.1 Introduction

As reported in Chapter 3, the main phenolic acids present in sugar cane juice

are caffeic acid (CaA), p–coumaric acid (pCoA) and ferulic acid (FeA), which are

classed as hydroxycinnamic acids (HCAs). Thus, the aim of this chapter was to

determine the optimum conditions and develop models for the rapid degradation of

these colour precursors by the Fenton oxidation process.

In the first section, Section 4.3.1, a preliminary investigation compared the

performance of the Fenton process on the degradation of caffeic acid in aqueous

solution to that of H2O2 alone.

The outputs from the study were then used to identify the necessary process

parameters and their numeric constraints for the development of a mathematical

model (Section 4.3.2). Response surface methodology (RSM) and central composite

experimental design were used to determine the optimum conditions for the

degradation of CaA. Also, the model was used to predict the optimum conditions for

the degradation of caffeic acid at particular stages of the sugar manufacturing process.

In Section 4.3.3, the study builds on the results and observations from the

previous sections by examining the degradation of a mixture of three HCAs; CaA,

pCoA and FeA, using the Fenton oxidation process in the presence and absence of

sucrose. Multi-response surface methodology (MRSM) was used for modelling and

optimisation of process parameters for the degradation process by examining

individual and interactive influences of the parameters. The rigorous optimisation

process undertaken in this study was to accurately determine the exact amounts of

phenolic acids degraded under the chosen Fenton process conditions, as any excess

iron would result in an increase in the amount of iron sludge and colour formed

during processing.

75



All chemicals purchased were of AR grade and used as supplied without

further purification. Solvents for chromatographic analyses were of super gradient

HPLC grade from Scharlau (Sentmenat, Spain). Solutions were prepared using

Milli-Q water from a Millipore system (Bedford, MA, USA) with a resistivity of 18.2

MΩ.cm.

The phenolic acids (CaA, pCoA and FeA), fructose, glucose, lactose and

sucrose were purchased from Sigma-Aldrich (St. Louis, MO, USA). Ferrous sulfate

heptahydrate (FeSO4·7H2O), glacial acetic acid, H2O2 (30% (w/v)), potassium

permanganate, sodium acetate, sodium hydroxide, sodium oxalate and sulfuric acid

were obtained from Ajax Finechem (Seven Hills, NSW, Australia). Ethanol

(absolute) was supplied from Merck (Darmstadt, Germany). Stock solutions of HCAs

(i.e., CaA, pCoA and FeA) were prepared individually by dissolution in degassed

ethanol solution (50% (v/v)) and stored at 4.0 °C, unless otherwise stated.

4.2.2 Catalytic and Non-catalytic Oxidation of Caffeic Acid

Caffeic acid solution (55.5 mM) was prepared by dissolving CaA in degassed

absolute ethanol solution (50% (v/v)). Aqueous Fe(II) solution (179 mM) was

prepared by dissolving solid FeSO4·7H2O in Milli-Q water. Dilute H2O2 solution

(147 mM) was prepared from stock H2O2 with Milli-Q water and standardised

iodometrically. The materials were used to prepare a series of solutions with a final

concentration of CaA (1.11 mM), Fe(II) (0 or 0.72 mM) and H2O2 (2.94 or 11.8 mM)

according to the sample matrix given in Table 4.1.

76

Table 4.1 Volumes of reagents (mM) used for the degradation of CaA.

Sample Water

(μL)

CaA

(μL)

Fe(II)

(μL)

H2O2

(μL)

Total

(μL)

Final [H2O2]

(mM)

Non-catalytic oxidation

Control 49,000 1,000 0 0 50,000 0

Test 1 48,000 1,000 0 1,000 50,000 2.94

Test 2 45,000 1,000 0 4,000 50,000 11.8

Catalytic oxidation

Control 48,880 1,000 200 0 50,000 0

Test 3 47,800 1,000 200 1,000 50,000 2.94

Test 4 44,800 1,000 200 4,000 50,000 11.8

Reactions were carried out in 50 mL Erlenmeyer flasks at ambient

temperature. The procedure for the catalytic oxidation can be described as follows:

(i) adjusting the pH to 3.0, 4.0 or 5.0 of the CaA solution using 0.01 M H2SO4 or

0.1 M NaOH; (ii) addition of Fe(II); (iii) addition of H2O2; and (iv) the reaction

allowed to run for up to 30 min with continuous magnetic stirring (280 rpm). The

procedure for the non-catalytic oxidation was identical with the exceptions that no

Fe(II) was added and that the reaction was allowed to run for up to 60 min. The pH

was measured using a Radiometer Analytical MeterLab PHM 220 pH meter (Lyon,

France). Aliquots (1 mL) were taken at 5 min intervals, diluted 10-fold and analysed

spectrophotometrically. Spectrophotometric measurements were conducted at

wavelengths ranging between 190 nm and 800 nm on a GBC Cintra 40 double beam

UV/Vis spectrophotometer using cells of 1.0 cm path length. Data acquisition was

performed using the GBC Spectral 1.50 software package.

4.2.3 Fenton Oxidation Reactions for Caffeic Acid Degradation

Reactions were carried out in 10 mL glass scintillated reaction vessels housed

in an 18971 Pierce Reacti-Therm heating/stirring module (Rockford, IL, USA) with

continuous magnetic stirring (280 rpm) (Figure 4.1). In each run, a predetermined

amount of Milli-Q water, sucrose and CaA were added to the reaction vessel and the

77

whole adjusted to the desired pH value with 0.01 M H2SO4 or 0.1 M NaOH. Known

amounts of FeSO4·7H2O and H2O2 solutions were added to achieve a final volume of

10 mL. The reaction was initiated as soon as H2O2 was added. For pH

measurements, a Hach H160 portable pH meter (Loveland, CO, USA) with a Eutech

Instruments glass pH electrode (Singapore) was used. Temperature was monitored

using a Comark C9001 thermometer probe (Sheffield, UK). At the required time of

sampling, 1.0 mL of the solution was taken, diluted 10-fold to quench the reaction and

measured immediately spectrophotometrically at 320 nm on a GBC Cintra 40 double

beam UV/Vis spectrophotometer (Braeside, VIC, Australia) using cells of 1.0 cm path

length. Data acquisition was performed using the GBC Spectral 1.50 software

package.

Figure 4.1 Schematic representation of heating block used for the Fenton

oxidation process.

78

4.2.4 Fenton Oxidation Reactions for the Degradation of Hydroxycinnamic Acid

Mixtures

The procedure for the Fenton oxidative degradation of HCA mixtures is

similar to that described in Section 4.2.3. In each run, a predetermined amount of

Milli-Q water, sucrose and each HCA (equivalent mg/L concentration) were added to

the reaction vessel. Known amounts of FeSO4·7H2O (50 mM, 0.498 mL) and H2O2

(500 mM, 0.150 mL) solutions were added to achieve a final volume of 10 mL and a

final concentration of 2.49 mM and 7.50 mM, respectively. The working molar ratio

of 1:15 (Fe(II)/H2O2) for the Fenton reaction of HCA mixtures was chosen based on

the optimum molar ratio of 1:13 for CaA solutions (cf. Section 4.3.2). The reaction

was initiated as soon as H2O2 was added. At 2 min, 3 mL of the solution was taken,

diluted 10-fold to quench the reaction and kept frozen. Samples were defrosted and

prepared for instrumental analysis.


HPLC-DAD. The proportion of each HCA degraded was monitored by

reversed-phase HPLC-DAD. The analysis was performed on a Hewlett Packard

HP/Agilent 1100 Series HPLC system (G1379A micro-degasser, Japan; G1311A

quaternary pump, Germany; G1313A ALS, Germany; G1315B DAD, Germany)

using a Waters Symmetry C18 column (150 × 3.9 mm i.d.) with a Waters Guard-Pak

guard holder containing a Waters Guard-Pak Resolve C18 guard insert (10 μm)

(Milford, MA, USA). The mobile phase consisted of 1.0% (v/v) acetic acid in water

(as eluent A) and methanol (as eluent B). The gradient program was as follows:

20% B to 25% B (5 min), 25% B to 50% B (15 min) and 50% B to 20% B (5 min).

Simultaneous detection at specific wavelengths (280 nm and 320 nm) subtracted

against a reference wavelength (620 nm). Aliquots of samples were membrane

filtered (0.45 μm) prior to injection into the HPLC system. Injection volume for all

samples was 50 μL; column temperature was ambient; flow rate was 1.0 mL/min and

run time was 25 min. After each run, the chromatographic system was equilibrated

for 5 min. Data acquisition was performed using the Agilent ChemStation (Rev.

A.09.03) software package. Identification of peaks was based on the conformance of

UV/Vis spectra and retention times with the corresponding authentic standards.

79

HPAEC-PAD. Sucrose and reducing sugar contents in the reaction mixtures

were monitored by high-performance anion exchange chromatography with pulsed

amperometric detection (HPAEC-PAD). The analysis was performed on a Waters

HPLC system (Milford, MA, USA) equipped with a 626 pump, a 600S controller, a

717plus autosampler and a 2465 electrochemical detector (fitted with a solid-state

Ag/AgCl reference electrode and a gold working electrode). The waveforms:

E1 = + 0.08 V for 0.4 s; E2 = +0.73 V for 0.4 s and E3 = –0.57 V for 0.2 s were

employed with a PAD intensity of 10 μA. Aliquots of samples were diluted 100-fold,

membrane filtered (0.45 µm) and injected (20 µL) on a Dionex CarboPac PA-1 guard

column (50 × 4 mm i.d.) attached to a Dionex CarboPac PA-1 anion exchange column

(250 × 4 mm i.d.) (Waltham, MA, USA). The columns were equilibrated at 27 °C.

The sugars were eluted isocratically with 150 mM NaOH (sparged with helium at

30 mL/min) at a flow rate of 1.0 mL/min. Data acquisition was performed using the

Waters Empower 2 (Build 2154) software package. Quantification of sugars was

carried out by external calibration using standard solutions of sucrose, glucose and

fructose in combination with lactose (as an internal standard).

4.2.6 Performance Assessment of the Fenton Oxidation Process

The efficiency of the Fenton process on the degradation of CaA, pCoA and

FeA was determined based on the change in absorbance of the corresponding HPLC

chromatographic peak using Equation 4.1:

0

0

% CaA, CoA or FeA degradation = 100tA Ap

A

(4.1)

where, A0 initial absorbance of HCA in mAU (at t = 0 min)

At absorbance of HCA in mAU at time of aliquot taken (at t = 2 min)

80

4.2.7 Design of Experiments

Design of experiments (DOE), mathematical modelling and optimisation of

process parameters were evaluated using the Stat-Ease, Inc. Design-Expert 7.0.0

software package (Minneapolis, MN, USA). Two experimental designs were

developed for the two separate batch Fenton oxidation reaction studies. The first

DOE was developed for the Fenton oxidation of CaA (cf. Section 4.2.3). Meanwhile,

the second DOE was developed for the Fenton oxidation of HCA mixtures (cf. Section

4.2.4).

Fenton Oxidation of Caffeic Acid (Design 1)

A face-centred central composite design (CCD) was used to evaluate the main

effect for each condition and the possible interaction effects on the residual stresses

between two factors. The factors (independent variables) used in this study were CaA

concentration (x1), sucrose concentration (x2), initial solution pH (x3), Fe(II) dosage

(x4), H2O2 dosage (x5), reaction temperature (x6) and reaction time (x7). The selected

response factor (dependent variable) for optimisation was % CaA degradation (y).

The coded and actual values of each factor and their levels for this experimental

design used in this study are shown in Table 4.2. The ranges for each parameter were

determined by preliminary experiments based on previous works published in the

literature (Pala and Erden, 2005; Nguyen and Doherty, 2012). The reaction time was

kept to 2 min in order to minimise sucrose degradation in order to allow treatment of

sugar cane process streams, where the main objective is to preserve the sucrose

content.

The design consisted of a 2k factorial augmented by 2k axial points and a

centre point, where k is the number of factors investigated (k = 7). For this study, a

total of 152 experiments were conducted in random order with 128 factorial points, 14

axial points and 1 centre point (duplicated 9 times for experimental error calculation).

81

Table 4.2 Coded and actual values of the experimental design for Design 1.

Coded levels of parameters

Notation Factor Unit –1 0 +1

A (x1) CaA mM 0.28 0.70 1.11

B (x2) Sucrose % (w/w) 0 17 34

C (x3) Solution pH 3.5 5.0 6.5

D (x4) Fe(II) dosage mM 0.18 0.45 0.72

E (x5) H2O2 dosage mM 2.21 6.62 11.03

F (x6) Temperature °C 35 65 95

G (x7) Time s 10 65 120

Fenton Oxidation of Hydroxycinnamic Acid Mixtures (Design 2)

A rotatable circumscribed CCD was used to evaluate the main effect for each

condition and the possible interactive effects on the residual stresses between two

variables. The process parameters (independent variables) used in this study were the

initial total HCA concentration (x1), the initial sucrose concentration (x2), the solution

pH (x3) and the reaction temperature (x4). The selected response factors (dependent

variables) for optimisation were % CaA degradation (y1), % pCoA degradation (y2),

% FeA degradation (y3) and % total HCA degradation (y4). The coded and actual

values of each variable and their levels for the experimental design used in the study

are shown in Table 4.3. The ranges for each parameter were determined by

preliminary experiments based on the previous experimental design (i.e., Design 1)

and were selected to closely mimic operating parameters during the processing of

sugar cane juice for raw sugar manufacture. Concentrations of HCAs vary depending

on season, region and type of cane and the method of harvesting (e.g., burnt cane,

green cane, whole crop cane), hence, an initial total HCA concentration range of

20–200 mg/L was chosen for this study in order to account for other HCAs and

phenolic compounds present in sugar cane juice.

82

Table 4.3 Coded and actual values of the experimental design for Design 2.

Coded levels of parameters

Notation Factor Unit –2 –1 0 +1 +2

A (x1) Total HCA mg/L 20 65 110 155 200

B (x2) Sucrose % (w/w) 0 3.75 7.50 11.25 15.00

C (x3) pH 4.50 3.75 5.00 5.25 5.50

D (x4) Temperature °C 25.00 31.25 37.50 43.75 50.00


centre point, where k is the number of factors investigated (k = 4). For this study, a

total of 54 experiments were conducted in random order with 16 factorial points (in

duplicate), 8 axial points (in duplicate) and 1 centre point (duplicated 5 times).

Duplicate runs were required for experimental error calculation.

4.2.8 Statistical Analysis

Analysis of variance (ANOVA) was used for model adequacy and analysis of

the experimental data. The quality of the fit polynomial model was expressed by the

regression coefficient, R2 and its statistical significance was checked using Fisher’s

F-test. Model terms were determined based on the significance of each term at a

confidence level of 95%.

4.2.9 Evaluation of the Interactions between Fe(II) and Hydroxycinnamic Acids

Studies were conducted to investigate the interaction between Fe(II) and each

of the HCAs in the presence and absence of sucrose using UV/Vis and attenuated total

reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. Sodium acetate

(100 mM) and acetic acid (100 mM) solutions were used to make buffer solutions

having pH values of 4.0 to 6.0. For each analysis, a predetermined amount of buffer,

sucrose and FeSO4·7H2O were added to achieve a final HCA concentration of

5.5 mM. Samples were diluted to the desired concentration and immediately

membrane filtered (0.45 μm) for analysis. The pH of each solution was checked

83

before and after dilution. The UV/Vis spectra were recorded on a Perkin Elmer

Lambda 35 double-beam UV/Vis spectrophotometer (Shelton, CT, USA) using cells

of 1.0 cm path length and at a wavelength range of 190–450 nm in 1.0 nm increments.

Data acquisition was performed using the Perkin Elmer UV WinLab (Ver. 2.85.04)

software package. Infrared absorbance spectra were obtained using a Thermo

Electron Nicolet Smart Endurance horizontal single bounce, diamond ATR accessory

on a Thermo Electron Nicolet Nexus 870 FTIR instrument fitted with a deuterated

triglycine sulfate detector (Madison, WI, USA). Spectra were recorded over the

4000–650 cm–1 range at 4 cm–1 resolution for 64 scans with an optical path difference

velocity of 0.6329 cm/s. Data acquisition and processing was performed using the

OMNIC 7.3 software package. The FTIR peaks were normalised with respect to the

main peak at 1045 cm–1.

A light brown precipitate was formed at pH ≥ 5.0 for all the acids with iron.

This precipitate was filtered using a polyvinyl chloride membrane filter (5 μm). It

was analysed by X-ray powder diffraction (XRD). Sample analysis was performed on

a PANalytical X’Pert PRO multi-purpose X-ray diffractometer (Almelo, Netherlands)

using Cu Kα radiation (λ = 1.5406 Å) at 40 kV and 40 mA. Patterns were recorded in

the 2θ range from 3.5° to 75° with a scan step size of 0.017° and a count time per step

of 50 s. Data was acquired and processed using the X’Pert Data Collector 2.2 and

MDI Jade 9.0 software packages respectively.


4.3.1 Catalytic and Non-catalytic Oxidation of Caffeic Acid in Aqueous Systems

The average concentration of CaA is approximately 20 mg/L on PJ obtained

from burnt cane, although an initial CaA concentration of 200 mg/L (i.e., 1.11 mM)

was chosen for the degradation studies in order to account for other phenolics and

colour precursors present in cane juice. The degradation of CaA in aqueous solutions

(at pH 3.0, 4.0 and 5.0) at 25 °C was studied with 2.94 or 11.8 mM H2O2.

84

The absorption spectra obtained for the degradation of CaA is shown in Figure

4.2. Two maxima at 320 nm and 292 nm are attributable to the CaA molecule (1) and

the deprotonated caffeate anion (2) respectively as shown in Scheme 4.1 (Cornard et

al., 2006).

Figure 4.2 Absorption spectra of CaA after the addition of 2.94 mM H2O2 at

pH 3.0 at 25 °C.

OH

OH

O

O

H

OH-

OH

OH

O

O

OH2+

Caffeic Acid Caffeate Anion

(1) (2)

Scheme 4.1

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

200 250 300 350 400

Ab

sorb

an

ce (

AU

)

Wavelength (nm)

t = 0 min

t = 30 min

t = 60 min

85

These two maxima were also present in reaction mixtures containing CaA and

Fenton’s reagent at pH 3.0, 4.0 and 5.0. After 60 min, 17% CaA was degraded at pH

3.0 with 2.94 mM H2O2. Working with solutions at pH 4.0 and 5.0, there was no

observable CaA degradation. At the higher H2O2 dosage of 11.8 mM, no further

reduction in absorbance was noticeable at either maximum even at pH 3.0. However,

there appears to be some degradation occurring at lower wavelengths, but this was not

conclusive. It is speculated that with the addition of 11.8 mM of H2O2 after the initial

reactions between the •OH radicals and CaA, there were subsequent recombination

reactions.

The degradation of CaA with Fenton’s reagent monitored at 320 nm is shown

in Figure 4.3. The reaction was virtually complete within 5 min. Within 30 min, 86%

of CaA was destroyed upon addition of 0.72 mM Fe(II) and 11.8 mM H2O2 at pH 5.0

(Figure 4.3a). At pH 3.0 and 4.0, the degradation of CaA was 62% and 66%

respectively. The degradation of the deprotonated caffeate anion was also observed.

The degradation ratio of the neutral and anionic forms was approximately 1:1; hence

the Fenton’s reagent is capable of attacking both forms of CaA.

The degradation trends were similar for both 2.94 mM and 11.8 mM H2O2

dosages with the latter having a larger decrease in absorbance (Figure 4.3b). At the

lower H2O2 concentration, approximately 70% degradation occurred after 30 min. A

faster degradation rate was observed at pH 5.0 for both H2O2 dosages despite a higher

initial absorbance. The higher initial absorbance is attributable to the chelating ability

of Fe(II)/Fe(III) on CaA to produce coloured complexes (Smith, 1983).

No other prominent peaks were observed across the spectral wavelength range

during the course of both experiments. This suggests that the degradation products

formed from the use of Fenton’s reagent are relatively LMW compounds with weak

chromophores or compounds without a chromophore.

In summary, the Fenton process is significantly more effective than H2O2 to

degrade CaA in aqueous systems. The Fenton process at 25 °C was optimum at pH

5.0 to degrade CaA in water, which better reflects the pH of sugar cane juices

anyway.

86

Figure 4.3 Degradation of CaA (measured at 320 nm) using Fenton’s reagent

at different initial pH at 25 °C. Concentrations of H2O2:

(a) 11.8 mM and (b) 2.94 mM.

0.00

0.25

0.50

0.75

1.00

-5 0 5 10 15 20 25 30 35

Ab

sorb

an

ce (

AU

)

Time (min)

pH 3.0

pH 4.0

pH 5.0

0.00

0.25

0.50

0.75

1.00

-5 0 5 10 15 20 25 30 35

Ab

sorb

an

ce (

AU

)

Time (min)

pH 3.0

pH 4.0

pH 5.0

(a)

(b)

87

4.3.2 Optimisation of Process Parameters for the Degradation of Caffeic Acid in

Sugar Solutions

Section 4.3.1 has shown that CaA can be degraded by the Fenton process.

Therefore, the aim of this work was to determine the optimal conditions and develop a

model for the degradation of CaA, both in aqueous and sucrose solutions using the

Fenton process. Response surface methodology, a powerful statistical tool, was used

for the experimental design and development of the model.

Regression Modelling and Statistical Analysis

Central composite design and RSM were used to evaluate the relationships

between the response (i.e., % CaA degradation) and the process parameters (i.e., H2O2

dosage, temperature and sucrose concentration). To achieve this, the experimental

data obtained from the experimental design using the constraints from Table 4.2, were

modelled by the system described through an empirical second-order polynomial

function (Montgomery, 2008):

2

0

1 1 1 1

k k

i ii i ij i ij

i i i i j

y x x x x

(4.2)

where, y predicted response (i.e., % CaA degradation)

β0 constant coefficient

βi linear coefficient

βii quadratic coefficient (for the independent factor i)

βij interaction effect coefficient (between independent factors i and j)

xij independent factors (i.e., process parameters shown in Table 4.2)

k number of process parameters investigated

ε random error

88

The ANOVA results are presented in Table 4.4. The analysis indicated that all

independent variables and some of their interactions were significant and contributed

to the degradation of CaA by Fenton oxidation. The model F-value of 22.28 implies

that the model is significant. There is only a 0.01% chance that a model F-value this

large could occur due to noise.

The model for % CaA degradation was improved after the exclusion of

insignificant coefficients (Table 4.4) is shown as follows:

CaA degradation (%)

y = 39.84 – 8.47A – 14.13B + 1.75C + 5.83D + 5.15E – 2.90F

+ 5.66G + 5.41AB + 3.20AD + 5.01AE – 2.35BD – 2.62BE

– 4.02BF – 1.81BG + 4.09CD – 2.35CF – 1.96DG – 2.55FG

+ 16.65B2

(4.3)

Based on the coefficients in Equation 4.3, it is evident that % CaA degradation

increases with solution pH (C), Fe(II) dosage (D), H2O2 dosage (E) and reaction time

(G) but decreases with initial CaA concentration (A), sucrose concentration (B) and

reaction temperature (F). Amongst the variables, key interaction effects between

initial CaA and sucrose concentrations (AB), CaA and Fe(II) (AD), CaA and H2O2

(AE), sucrose and Fe(II) (BD), sucrose and H2O2 (BE), sucrose and temperature (BF),

sucrose and time (BG), pH and Fe(II) (CD), pH and temperature (CF), Fe(II) and time

(DG) and temperature and time (FG) are also observed.

The response surface quadratic model diagnostics for % CaA degradation is

summarised in Table 4.5. A satisfactory R2 coefficient of 0.87 meant that the model

explains 87% of the variability in the data. The predicted R2 of 0.75 is in reasonable

agreement with the adjusted R2 of 0.83, and a plot of predicted values of % CaA

degradation against the observed values was degenerated as shown in Figure 4.4. As

a reasonable linear relationship was obtained, Equation 4.3 (i.e., the quadratic model)

is suitable for predicting the % degradation of CaA.

89

Table 4.4 Analysis of variance (ANOVA) results for response surface

quadratic model terms for CaA degradation.*

Source SS df Mean sq. F-value p-value Remarks

Model 67,843.69 35 1,938.39 22.28 < 0.0001 Significant

A 9,319.02 1 9,319.02 107.11 < 0.0001 Significant

B 25,955.48 1 25,955.48 298.33 < 0.0001 Significant

C 399.74 1 399.74 4.59 0.0342 Significant

D 4,414.70 1 4,414.70 50.74 < 0.0001 Significant

E 3,446.42 1 3,446.42 39.61 < 0.0001 Significant

F 1,094.64 1 1,094.64 12.58 0.0006 Significant

G 4,162.00 1 4,162.00 47.84 < 0.0001 Significant

AB 3,642.45 1 3,642.45 43.01 < 0.0001 Significant

AC 85.34 1 85.34 0.98 0.3240

AD 1,310.89 1 1,310.89 15.07 0.0002 Significant

AE 3,213.26 1 3,213.26 36.93 < 0.0001 Significant

AF 153.53 1 153.53 1.76 0.1867

BC 87.00 1 87.00 1.00 0.3194

BD 72.75 1 72.75 0.84 0.3624

BE 707.20 1 707.20 8.13 0.0052 Significant

BF 879.30 1 879.30 10.11 0.0019 Significant

BG 2,070.87 1 2,070.87 23.80 < 0.0001 Significant

BH 419.35 1 419.35 4.82 0.0301 Significant

CD 2,145.37 1 2,145.37 24.66 < 0.0001 Significant

CE 91.71 1 91.71 1.05 0.3067

CF 705.64 1 705.64 8.11 0.0052 Significant

CG 84.64 1 84.64 0.97 0.3260

DE 10.29 1 10.29 0.12 0.7315

DF 15.19 1 15.19 0.17 0.6768

DG 489.94 1 489.94 5.63 0.0193 Significant

EF 289.07 1 289.07 3.32 0.0709

EG 50.24 1 50.24 0.58 0.4489

FG 829.72 1 829.72 9.54 0.0025 Significant

A2 88.84 1 88.84 1.02 0.3143

90

B2 644.82 1 644.82 7.41 0.0075 Significant

C2 264.65 1 264.65 3.04 0.0838

D2 64.11 1 64.11 0.74 0.3924

E2 298.99 1 298.99 3.44 0.0663

F2 158.59 1 158.59 1.82 0.1796

G2 0.25 1 0.25 0.0029 0.9571

Residual 10,092.40 116 87.00

Lack of fit 10,092.37 107 94.32 26,870 < 0.0001 Significant

Pure error 0.032 9 0.0035

Corr. total 77936.09 151

*SS (Sum of Squares), df (degrees of freedom), Mean sq. (Mean Square),

Corr. (Corrected)

Table 4.5 Regression diagnostics for the response surface quadratic model

for CaA degradation.

Criteria

Standard deviation 9.33

Mean 43.40

Coefficient of variance (CV) (%) 21.49

Predicted residual sum of squares (PRESS) 19,391.88

R2 0.87

Adjusted R2 0.83

Predicted R2 0.75

Adequate precision 21.00

91

Figure 4.4 Plot of predicted and experimental (actual) values for the

degradation (%) of CaA.


degradation data.

92

The residuals from the least squares of fit are important for judging model

adequacy. Through constructing the plot of studentised residuals versus the normal

percentage of probability as shown in Figure 4.5, a check was made for the normality

assumption, which was found to be satisfied for the % CaA degradation as the

residual plots approximated a straight line.

Interaction Effects between Process Parameters

For the graphical interpretation of the interactions between % CaA

degradation and the process parameters, three-dimensional (3D) surface plots of the

regression model (Equation 4.3) were used. These plots are shown in Figures 4.6 and

4.7 and some of the interactions are significant as the curvature of the 3D surfaces

was obvious.

Influence of Initial CaA Concentration

It is observed that, at a given time, a higher initial CaA concentration results in

lower degradation (Figure 4.6a). However, in relation to the reaction rate, a higher

initial CaA concentration will result in a higher degradation rate of CaA. In other

words, increasing the concentration of CaA involves higher uptake of •OH radicals

produced from decomposed H2O2. It is presumed that the degradation efficiency of

CaA in a mixture of other phenolic acids (and other juice components) would

decrease because of competing reactions between •OH radicals and the other phenolic

compounds. This will be further investigated in the next section of this chapter

(cf. Section 4.3.3).

Influence of Sucrose Concentration

The influence of sucrose concentration of the oxidation of CaA was

investigated as shown in Figure 4b and 4c. The addition of sucrose clearly inhibited

the oxidation of CaA. The results show that up to 61% of CaA was degraded at

13% (w/w) sucrose, the concentration typically encountered in factory cane MJs. The

93

H2O2 concentration has a greater negative influence on the amount of CaA degraded

relative to temperature effect. Morelli et al., (2003) investigated whether the •OH

radicals generated by the Fenton process were scavenged by simple carbohydrates.

Their results not only show the scavenging ability of simple sugars but show that

disaccharides such as maltose and sucrose were more effective than monosaccharides

in removing •OH radicals. So, the reduced effectiveness of the Fenton process for

CaA degradation in the presence of sucrose is related to the reduction of available

•OH radicals.


function of (a) CaA and Fe(II); (b) sucrose and H2O2; (c) sucrose

and temperature; and (d) pH and Fe(II). Variables: CaA

(1.11 mM); sucrose (0% (w/w)); pH (5.0); Fe(II) (0.45 mM); H2O2

(6.62 mM); temperature (35 °C) and time (120 s).

Design-Expert® Software

% CA Degradation96.8358

0.146016

X1 = A: CAX2 = D: Fe(II)

Actual FactorsB: Sucrose = 0.00C: pH = 5.00E: Peroxide = 6.62F: Temp. = 35.00G: Time = 120.00

0.28

0.49

0.70

0.90

1.11

0.18

0.31

0.45

0.58

0.72

50

65

80

95

110

%

CA

Degra

datio

n

A: CA D: Fe(II)



0.146016

X1 = B: SucroseX2 = E: Peroxide

Actual FactorsA: CA = 1.11C: pH = 5.00D: Fe(II) = 0.45F: Temp. = 35.00G: Time = 120.00

0

9

17

26

34

2.21

4.41

6.62

8.82

11.03

25

40

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%

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0.146016

X1 = B: SucroseX2 = F: Temp.

Actual FactorsA: CA = 1.11C: pH = 5.00D: Fe(II) = 0.45E: Peroxide = 6.62G: Time = 120.00

0

9

17

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Actual FactorsA: CA = 1.11B: Sucrose = 0E: Peroxide = 6.62F: Temp. = 35G: Time = 120.00

3.50

4.25

5.00

5.75

6.50

0.18

0.31

0.45

0.58

0.72

45

55

65

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%

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Degra

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C: pH D: Fe(II)

% C

aA

Degra

datio

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A: CaA

(a)

(c)

(b)

(d)

% C

aA

Degra

datio

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% C

aA

Degra

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% C

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E: H2O2

94

Influence of Solution pH

Experiments were conducted at initial pH of 3.5, 5.0 and 6.5. Maximum CaA

degradation was observed at pH 4.5–5.5 (Figure 4.6d and 4.7a). This is in line with

the results of Tang and Huang (1996) and Deng (2007). At higher pH values, Fe(III)

produced from Fe(II) oxidation precipitates as oxyhydroxides instead of being

regenerated back to Fe(II). Hence, the total amount of Fe(II) required to catalyse the

decomposition of H2O2 to produce the reactive •OH radicals is reduced (Cortez et al.,

2011). As such, the lower degradation of CaA at pH 6.5 is mainly attributable to the

generation of reduced amounts of •OH radicals in comparison with •OH radicals

generated at pH 3.5–5.5. Also, H2O2 is unstable under alkaline conditions and itself

may rapidly decompose to water and oxygen (Chang et al., 2010).

Influence of Fe(II) and H2O2

The interactive effects of both Fe(II) and H2O2 are shown in the surface plot of

Figure 4.7b. A greater proportion of CaA is degraded with increasing Fe(II) and H2O2

concentrations. The availability of increasing amounts of H2O2 will result in an

increase in the proportion of •OH radicals formed as Fe(II) can readily be generated

by Fe(III). Also, increasing the concentration of Fe(II) will result in an increase in the

amount of H2O2 formed. However, there is an optimum molar ratio of Fe(II) to H2O2

required for the generation of •OH radicals. In this study, the optimum molar ratio of

Fe(II) to H2O2 for the degradation of CaA is 1:13. The value mentioned in the

literature varied from 1:1 to 1:400 as different feed compositions and operating

conditions were examined (Tang and Huang, 1997; Kitis et al., 1999; de Souza et al.,

2006). According to the stoichiometric equation (Equation 4.4), a molar ratio of 1:18

is required for the complete mineralisation of CaA by H2O2.

C9H8O4 + 18H2O2 9CO2 + 22H2O (4.4)

So, the present result confirms the catalytic influence of Fe(II) on CaA

degradation.

95

Figure 4.7 Three-dimensional surface plots of CaA degradation (%) as

function of (a) pH and H2O2; (b) Fe(II) and H2O2; (c) H2O2 and

temperature; and (d) H2O2 and time. Variables: CaA (1.11 mM);

sucrose (0% (w/w)); pH (5.0); Fe(II) (0.45 mM); H2O2 (6.62 mM);

temperature (35 °C) and time (120 s).

Influence of Temperature

The effect of temperature on CaA degradation was studied at 35 °C, 65 °C and

95 °C (Figures 4.6c and 4.7c). Degradation of CaA occurred at a faster rate with

increasing temperature. This is because raising the temperature increased the

decomposition rate of H2O2 and hence the formation of reactive •OH radicals (Sun et

al., 2009). However, the decomposition of H2O2 is not directly linked to the amount

of CaA degraded, because in addition to the formation of •OH radicals, at higher



0.146016

X1 = C: pHX2 = E: Peroxide

Actual FactorsA: CA = 1.11B: Sucrose = 0D: Fe(II) = 0.45F: Temp. = 35G: Time = 120.00

3.50

4.25

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8.82

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CA

Degra

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Actual FactorsA: CA = 1.11B: Sucrose = 0C: pH = 5.00F: Temp. = 35G: Time = 120.00

0.18

0.31

0.45

0.58

0.72

2.21

4.41

6.62

8.82

11.03

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%

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D: Fe(II) E: Peroxide Design-Expert® Software


0.146016

X1 = E: PeroxideX2 = F: Temp.

Actual FactorsA: CA = 1.11B: Sucrose = 0C: pH = 5.00D: Fe(II) = 0.45G: Time = 120.00

2.21

4.41

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8.82

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2.21

4.41

6.62

8.82

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38

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120

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(a)

(c)

(b)

(d)

% C

aA

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% C

aA

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datio

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% C

aA

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96

temperatures non-reactive species such as H2O and O2 are formed (Rodrigues et al.,

2009a). These counteractive effects are clearly illustrated in Figure 4.6c where

maximum degradation of CaA is obtained at 35 °C. However, the degradation of

CaA at 95 °C is more effective than at 65 °C because of the contributing effect

resulting from the thermal degradation of CaA. It has been reported by Kulik et al.

(2011) that the decarboxylation of CaA and other HCAs (e.g., pCoA and FeA) occurs

at temperatures > 70 °C.

Influence of Time

Figure 4.7d shows that reaction time has a positive effect on the degradation of

CaA. Maximum CaA degradation is achieved within 120 s, as there was no increase

thereafter. The short degradation time obtained in this study implies that the Fenton

oxidation process will be suitably applied in a sugar factory for the degradation of

CaA and other phenolic compounds.

Sugars Analysis

During raw sugar manufacture, sucrose loss through inversion to glucose and

fructose, and degradation to organic acids are minimised to maintain sugar yield by

working at selected pH and temperatures. Sucrose degradation by Fenton oxidation

was evaluated by HPAEC-PAD (cf. Appendices, Table A1.2). The results showed

minimal losses of sucrose (< 0.01%) were present in reactions carried out at 35 °C

after 10 min. Conversion of sucrose to glucose and fructose was observed in

reactions carried out at 65 °C and 95 °C (< 1.0%), the latter showing higher amounts

of reducing sugars. This means that the Fenton process may only find applications in

the sugar manufacturing process at far lower temperatures.

97

Model Validation and Optimisation

Numeric optimisation was used to determine the optimum process parameters

for CaA degradation. The optimum and worst conditions for CaA degradation were

obtained on the basis of the model (Equation 4.3) and the desirability function. The

desirability function is expressed as a numeric value and denotes the degree of

importance in obtaining the desired target response value. To validate the accuracy

and robustness of the predicted model and the reliability of the obtained conditions,

additional experiments were carried out under those conditions, as well as randomly

selected conditions within the ranges investigated. As shown in Table 4.6, the

experimental values of the optimum and worst conditions agree well with the

predicted values.


degradation of CaA and model verification.

Experiments*

1 2 3 4

CaA (mM) 1.11 1.11 1.11 1.11

Sucrose (% (w/w)) 0 0 14 34

pH 5.1 5.5 5.1 3.5

Fe(II) (mM) 0.68 0.72 0.64 0.18

H2O2 (mM) 8.88 9.44 8.47 2.21

Temperature (°C) 95 35 95 64

Time (s) 120 120 120 120

Observed degradation (%) 91 80 59 10

Predicted degradation (%) 92 85 62 11

Error 1.00 5.00 3.00 1.00

Standard deviation 0.71 3.53 2.12 0.71

Desirability function 0.95 0.87 0.64 0.89

*Experiments: (1) Optimum; (2) Optimum without thermal degradation; (3) Optimum

with 14% (w/w) sucrose; and (4) worst case.

98

The experimental values of randomly selected conditions are shown in Table

4.7. The low error in the experimental and predicted values indicates good agreement

of the results. The desirability functions obtained with solutions containing sucrose

were comparatively less than the values obtained in the absence of sucrose (Table

4.7).

Table 4.7 Model verification of optimised conditions under randomly

specified constraints for CaA degradation.

Experiments

5 6 7

CaA (mM) 1.11 1.11 1.11

Sucrose (% (w/w)) 0 14 34

pH 5.0 5.0 5.0

Fe(II) (mM) 0.72 0.72 0.36

H2O2 (mM) 4.41 2.21 2.21

Temperature (°C) 35 35 76

Time (s) 120 120 120

Observed degradation (%) 67 31 31

Predicted degradation (%) 69 33 29

Error 2.00 2.00 2.00

Standard deviation 1.41 1.41 1.41

Desirability function 0.84 0.57 0.55

The applicability of the proposed model was also investigated using the raw

sugar processing constraints of a typical Australian sugar cane factory. On the basis

of the colour profile across the sugar manufacturing stage as shown in Figure 2.8

(Eggleston et al., 2003), to reduce colour in raw sugar, colour removal strategies

should be targeted at MJ (i.e., juice prior to incubation), PJ (i.e., prior to liming)

and/or on juices during the evaporation stage. From the information obtained from

the model, sugar process streams operating at temperatures > 95 °C (because of

99

sucrose degradation) and/or at sucrose concentrations > 34% (w/w) may not be

suitable to be treated with the Fenton oxidation process.

Table 4.8 shows results obtained for synthetic juice solutions, under

processing conditions similar to that of MJ, PJ and juice from the third effect of a

quintuple evaporator set. It shows that the best result is obtained with MJ followed by

PJ. A higher error in the experimental and predicted values for the optimised

conditions for the optimised conditions for the third effect juice, compared with the

conditions of other juice process streams, was observed. It is probable that solution

pH may have contributed to the inaccuracy of the prediction as it is outside the range

used to develop the proposed model (Equation 4.3).

Table 4.8 Model verification of optimised conditions in synthetic juice

solutions under specified constraints of selected sugar process

streams for CaA degradation.

Experiments

Mixed Juice Primary Juice Third Effect

CaA (mM) 1.11 1.11 1.11

Sucrose (% (w/w)) 13 17 30

pH 5.4 5.4 6.8

Fe(II) (mM) 0.68 0.66 0.64

H2O2 (mM) 8.67 8.62 8.59

Temperature (°C) 35 76 94

Time (s) 120 120 120

Observed degradation (%) 61 49 27

Predicted degradation (%) 62 52 41

Error 1.00 3.00 14.0

Standard deviation 0.71 2.12 9.90

Desirability function 0.64 0.54 0.42

100

The aforementioned results have shown that the Fenton process is reasonably

effective in degrading CaA in sucrose solutions. The reduced effectiveness of the

Fenton process for the degradation of CaA for these systems is related to the reduction

of available •OH radicals by the scavenging action of sucrose (Morelli et al., 2003).

Despite the free radical scavenging ability of sucrose, minimal losses of sucrose

(< 0.01%) were obtained after 2 min of treatment.

4.3.3 Degradation of Hydroxycinnamic Acid Mixtures

The work described in this section builds on the results previously discussed in

Sections 4.3.1 and 4.3.2 by examining the degradation of a mixture of the three main

HCAs present in Australian sugar cane juice (viz., CaA, pCoA and FeA) using the

Fenton process in the presence and absence of sucrose. The results obtained were

used to develop a model for the degradation of each individual HCA within a mixture

as well as a model for total HCA degradation. No previous study has reported on the

optimisation of the degradation process of individual acids within a mixture of other

phenolic acids by the Fenton process, nor examined the interactive effects of various

operating parameters on the degradation of each acid.

Optimal Data Transformation and Test for Normality

Rotatable CCD and RSM were used to investigate the relationships between

the response factors (dependent variables) and the process parameters (independent

variables). In order to achieve this, an empirical second-order polynomial function

identical to Equation 4.2, for each response factor was used to fit the experimental

results obtained.

The assumption used to estimate the response based on the model given in

Equation 4.2 is that the random error terms (ε) for all levels of the independent factors

are distributed normally and independently with a mean zero and a common variance

(Tunali and Batmaz, 2000). Graphical residual analysis was used to verify the

adequacy of different aspects of the model. The residuals from the least squares fit

are important for judging model adequacy. A normal probability plot of residuals

101

based on the experimental data obtained for CaA degradation (Figure 4.8) indicates a

non-linear pattern in the middle of the trend line, and short tails with the first and last

few points showing increasing departure from the trend line.


degradation data before power transformation.

To address the non-linearity of these plots, the Box-Cox power transformation

was used to improve linearity. The power transformation on the predicted response

can be described as follows (Box and Cox, 1964):

1 0

ln 0

y

y

y

(4.5)

where λ indicates the power to which all data should be raised. The initial value of λ

in the standard quadratic function (i.e., Equation 4.2) is λ = 1.00.

102

To determine the λ value for each response, a Box-Cox plot was used as a

guide for the selection of the optimised λ value for the power transformation of the

experimental data. Figures 4.9 and 4.10 show the Box-Cox plots for each response

investigated. From the Box-Cox plots for the degradation of pCoA (Figure 4.9b) and

FeA (Figure 4.10a), the recommended λ values ranged from 0.70 to 2.40 and 0.59 to

2.23, respectively at a 95% confidence interval. On the other hand, the λ value range

within the 95% confidence interval were not shown for the degradation data of CaA

(Figure 4.9a) and total HCA (Figure 4.10b), due to the values being outside the

λ = ± 3.00 limits. Hence, the optimum λ values used to transform the CaA and total

HCA degradation were both maximised at λ = 3.00. For pCoA and FeA degradation,

the optimum λ values were determined by observing the minimum of the curve, which

was 1.56 and 1.43 respectively.

Using the optimised λ values, the normal probability plot for each response

surface model shown in Figures 4.11 and 4.12 indicate improved linearity of data

points. There are only a minimal number of data points deviating from the line of fit.

The data for all fitted response surface models show good correspondence to a normal

distribution and validated the normality assumption.

103

Figure 4.9 Box-Cox plots of (a) CaA and (b) pCoA degradation data for the

determination of the optimised power transformed response

surface models.

(a)

(b)

104

Figure 4.10 Box-Cox plots of (a) FeA and (b) total HCA degradation data for

the determination of the optimised power transformed response

surface models.

(a)

(b)

105


(a) CaA and (b) pCoA degradation data after power

transformation.

(a)

(b)

106


(a) FeA and (b) total HCA degradation data after power

transformation.

(a)

(b)

107

Regression Modelling and Statistical Analysis

On the basis of the sequential model sum of squares (Type I), the power

transformed response surface models for CaA (y1), pCoA (y2), FeA (y3) and total

HCA (y4) degradation were selected based on the highest order polynomial, where the

additional model terms were significant and the models were not aliased. The data

obtained for all four responses fit a quadratic polynomial function.

In each model, there are some unimportant model terms that should be

removed to improve the accuracy of fitting. In this study, the significant coefficients

of the models were identified using ANOVA statistics and stepwise regression.

Stepwise regression involves the selection of the most appropriate independent

variables for a regression model. In this case, a subset of variables from the full set is

determined. The stepwise regression method is a combination of forward selection

and backward elimination (regression) statistical methods. From the full set of

available variables, the stepwise procedure builds or depletes the regression model,

one variable at a time. Sequentially, variables are added (i.e., forward selection) at an

alpha-to-enter significance level of 0.1 and removed (i.e., backward elimination) at an

alpha-to-exit significance level of 0.1 until an added variable does not yield a Student

t–test probability (p–value) of ≤ 0.1. The chosen stepwise alpha range applied to all

four response surface models should result in final models with significant model

terms included at the approximate 95% confidence level.

The ANOVA results for the partial sum of squares (Type III) for the four

response surface reduced quadratic models after stepwise regression are shown in

Tables 4.9–4.12. The analysis indicates that most independent variables and some of

the interactions are significant and contribute to the degradation of the HCAs. The

model F-values of 43.30, 44.14, 88.37 and 19.03 for CaA, pCoA, FeA and total HCA

degradation respectively, imply that the models are significant. There is only a 0.01%

chance that a model F-value this large could occur due to noise. The lack-of-fit

F-values of 1.40 and 1.38 for the pCoA and FeA models in that order imply that the

lack of fit is not significant relative to the pure error. There is a 21% and 23% chance

respectively that the lack-of-fit F-values this large would occur due to noise. Non-

significant lack-of-fit is good as it confirms the predictability of the model. On the

other hand, the lack-of-fit F-values of 34.50 and 2.65 for CaA and total HCA models

108

respectively, imply that the lack-of-fit is significant. A significant lack-of-fit is

undesirable as the proposed models do not fit the data well. This may be because the

experimental data of CaA degradation showed little variation (p < 0.0001) under the

constraints of the experimental design compared to pCoA and FeA degradation data.

Therefore, it also affected the lack-of-fit of the total HCA degradation model

(p = 0.0138). Despite this, further ANOVA statistics (discussed later) demonstrate

that the data is suitable for the modelling and prediction of CaA and total HCA

degradation.

Model terms with a p–value < 0.0500 indicate model terms are significant at

the 95% confidence level. Values > 0.1000 indicate the model terms are insignificant

at the 90% confidence level and are removed from the proposed models via stepwise

regression, with the exception of the first-order temperature model term for all

models. Temperature was regarded as statistically insignificant but was added to all

models to make each model hierarchical. In other words, parent (i.e., first-order)

model terms are added to the model to complete the family of any significant higher-

order (i.e., second-order) model terms.

The independent variables in the models were initial total HCA concentration,

initial sucrose concentration, solution pH and reaction temperature; and were coded A,

B, C and D respectively. The final empirical quadratic equations in terms of coded

factors for each response are as follows:

CaA degradation (%)

(y1)3 = 7.459 × 105 – 22685.04A + 87649.64B –1.893 × 105C

– 2787.88D + 38875.43BC + 25613.66BD – 48866.47B2

– 55229.82C2 + 21771.66D2

(4.6)

pCoA degradation (%)

(y2)1.56 = 452.03 – 25.39A – 112.96B + 56.46C – 9.29D + 25.34AB

+ 24.11CD + 51.51B2 – 13.11C2

(4.7)

109


reduced quadratic model for CaA degradation.*


Model 2.49 × 1012 9 2.77 × 1011 43.30 < 0.0001 Significant

A 2.47 × 1010 1 2.47 × 1010 3.87 0.0556

B 3.69 × 1011 1 3.69 × 1011 57.74 < 0.0001 Significant

C 1.72 × 1012 1 1.72 × 1012 269.21 < 0.0001 Significant

D 3.73 × 1008 1 3.73 × 1008 0.058 0.8101 Insignificant

BC 4.84 × 1010 1 4.84 × 1010 7.57 0.0086 Significant

BD 2.10 × 1010 1 2.10 × 1010 3.29 0.0766

B2 1.15 × 1011 1 1.15 × 1011 17.95 0.0001 Significant

C2 1.46 × 1011 1 1.46 × 1011 22.93 < 0.0001 Significant

D2 2.28 × 1010 1 2.28 × 1010 3.56 0.0657

Residual 2.81 × 1011 44 87.00

Lack of fit 2.66 × 1011 15 94.32 34.50 < 0.0001 Significant

Pure error 1.49 × 1010 29 0.0035

Corr. total 2.77 × 1012 53

Criteria

Standard deviation 8.00 × 1004

Mean 6.73 × 1005

CV (%) 11.88

PRESS 4.54 × 1011

R2 0.90

Adjusted R2 0.88

Predicted R2 0.84



Corr. (Corrected)

110


reduced quadratic model for pCoA degradation.*



A 3.09 × 104 1 3.09 × 104 10.90 0.0019 Significant

B 5.49 × 105 1 5.49 × 105 193.36 < 0.0001 Significant

C 1.53 × 105 1 1.53 × 105 53.93 < 0.0001 Significant

D 4.14 × 103 1 4.14 × 103 1.46 0.2333 Insignificant

AB 2.05 × 104 1 2.05 × 104 7.24 0.0100 Significant

CD 1.81× 103 1 1.81× 103 6.56 0.0139 Significant

B2 1.10 × 105 1 1.10 × 105 38.77 0.0001 Significant

C2 8.78 × 103 1 8.78 × 103 3.09 0.0855

Residual 1.25 × 105 44 87.00

Lack of fit 5.55 × 104 16 94.32 1.40 0.2121 Insignificant

Pure error 6.94 × 104 28 2.48 × 103

Corr. total 1.13E+06 52

Criteria


Mean 487.18

CV (%) 10.93

PRESS 1.82 × 105

R2 0.89

Adjusted R2 0.87

Predicted R2 0.84



Corr. (Corrected)

111


reduced quadratic model for FeA degradation.*



A 1.03 × 104 1 1.03 × 104 24.74 < 0.0001 Significant

B 2.02 × 105 1 2.02 × 105 485.42 < 0.0001 Significant

C 3.16 × 104 1 3.16 × 104 76.07 < 0.0001 Significant

D 528.86 1 528.86 1.27 0.2656 Insignificant

AB 1.92 × 104 1 1.92 × 104 46.22 < 0.0001 Significant

BD 2.70 × 103 1 2.70 × 103 6.49 0.0146 Significant

CD 1.24 × 103 1 1.24 × 103 2.98 0.0915

B2 2.89 × 104 1 2.89 × 104 69.46 < 0.0001 Significant

D2 4.57 × 103 1 4.57 × 103 11.01 0.0019 Significant

Residual 1.74 × 104 42 415.39

Lack of fit 7.57 × 103 15 504.72 1.38 0.2262 Insignificant

Pure error 9.88 × 103 27 365.76

Corr. total 3.48 × 105 51

Criteria


Mean 308.78

CV (%) 6.60

PRESS 2.81 × 104

R2 0.95

Adjusted R2 0.94

Predicted R2 0.92



Corr. (Corrected)

112


reduced quadratic model for total HCA degradation.*

Source SS df Mean Sq. F-value p-value Remarks


A 2.46 × 1010 1 2.46 × 1010 21.57 < 0.0001 Significant

B 1.16 × 1011 1 1.16 × 1011 102.20 < 0.0001 Significant

C 5.41 × 1009 1 5.41 × 1009 4.75 0.0349 Significant

D 8.13 × 1008 1 8.13 × 1008 0.71 0.4028 Insignificant

AB 1.11 × 1010 1 1.11 × 1010 9.79 0.0032 Significant

BC 9.27 × 1009 1 9.27 × 1009 8.14 0.0067 Significant

CD 6.14 × 1009 1 6.14 × 1009 5.39 0.0252 Significant

B2 4.20 × 1009 1 4.20 × 1009 3.68 0.0617

C2 1.89 × 1010 1 1.89 × 1010 16.62 0.0002 Significant

D2 6.11 × 1009 1 6.11 × 1009 5.37 0.0254 Significant

Residual 4.78 × 1010 42 415.39

Lack of Fit 2.72 × 1010 14 504.72 2.65 0.2262 Significant

Pure Error 2.06 × 1010 28 365.76

Corr. total 2.64 × 1011 52

Criteria

Standard Deviation 3.37 × 1004

Mean 2.68 × 1005

CV (%) 12.58

PRESS 8.04 × 1010

R2 0.82

Adjusted R2 0.78

Predicted R2 0.70

Adequate Precision 15.61


Corr. (Corrected)

113

FeA degradation (%)

(y3)1.43 = 274.79 – 14.82A – 69.60B + 25.99C – 3.36D + 24.97AB

– 9.36BD + 6.34 CD + 26.42B2 + 9.46D2

(4.8)

Total HCA degradation (%)

(y4)3

= 2.670 × 105 – 22911.69A – 49869.11B – 10752.76C

– 4168.93D + 19018.79AB + 17344.68BC +14113.81CD

+ 9351.73B2 – 19861.54C2 + 11289.82D2

(4.9)

The predicted R2 values of all response surface models are in reasonable

agreement with the adjusted R2 values, which show that the fitted models are

adequate. The accuracy of the models is shown in Figures 4.13 and 4.14, which

compares the predicted responses against the experimental data. As reasonable linear

relationships were obtained, Equations 4.6–4.9 are suitable for predicting the

degradation of CaA, pCoA, FeA and total HCA, respectively.

On the basis of the coefficients of the first-order model terms in Equations

4.6–4.9, it is evident that the degradation efficiency of all HCAs decreases with initial

total HCA concentration (A). Sucrose concentration (B) is the most influential

parameter with the highest coefficient in all equations and shows a negative influence

in pCoA and FeA degradation but a positive influence for CaA degradation. Also, the

degradation efficiency of pCoA and FeA increases with solution pH (C) but the

opposite is observed for CaA. Temperature (D) has a negative effect on all responses

but its minuscule coefficient has little effect on the respective response. Hence, this

model term is statistically insignificant and was only included in all of the equations

to make the models hierarchical.

For the degradation of mixtures (i.e., the acids combined), there are strong

interactions between total HCA concentration and sucrose (AB); sucrose and pH (BC);

and pH and temperature (CD).

114


the degradation (%) of (a) CaA and (b) pCoA.

(a)

(b)

115


the degradation (%) of (a) FeA and (b) total HCA.

(a)

(b)

116

Perturbation Analysis

Perturbation plots were analysed in order to further identify the most

influential variables on the degradation of each HCAs investigated in this study

(Figure 4.15). Sucrose concentration and solution pH appeared to be the most

influential parameters. Temperature showed an insignificant effect as expected,

whilst the initial total HCA concentration exhibited a consistent effect for the

degradation of each HCA.

As shown in Figure 4.15, the higher the initial total HCA concentration, the

lower the amount of each HCA degraded. The reason behind the decrease in

degradation efficiency is simply due to a higher uptake of •OH radicals by the

increased amounts of HCA molecules.

The presence of sucrose significantly affected the degradation efficiency of the

HCAs. The fate of sucrose during the degradation process was evaluated by HPAEC-

PAD (cf. Appendices, Table A1.3). The results showed up to 0.01% sucrose loss due

to complete mineralisation, as no glucose and/or fructose are detected. This is related

to the effective scavenging ability of sucrose in removing •OH radicals (Morelli et al.,

2003), and accounts for the decrease in degradation efficiency with increasing in

sucrose concentration for pCoA and FeA (Figures 4.15b and 4.15c), but not for CaA

(Figure 4.15a). The reason for the increased degradation efficiency of CaA with

increasing sucrose concentration, may be related as will be shown in the next

subsection, to a strong association between CaA and sucrose which increased with

increasing sucrose concentration.

Degradation of CaA decreases with increasing pH whereas the opposite was

observed for pCoA and FeA degradation. The reason for the results obtained with

pCoA and FeA is not known but may be related to the various species that exist in the

acid-base equilibria that influences the logarithmic acid dissociation constants (pKa’s)

of these acids.

117

Figure 4.15 Perturbation plots for the degradation (%) of (a) CaA; (b) pCoA

and (c) FeA. Coded values are shown for each factor: total

HCA (A); sucrose (B); pH (C) and temperature (D); and refer to

actual values listed in Table 4.3.

(a)

(b)

(c)

118

Complex Formation

In order to obtain insights into the apparent differences in the behaviour

among the three HCAs, the UV/Vis spectra of the individual acids, mixtures of each

acid with Fe(II) and mixtures of each acid with Fe(II) and sucrose at pH 4.0 to 6.0

were obtained. The UV/Vis spectra obtained with mixtures of FeA or mixtures of

pCoA were not dissimilar to that of their corresponding acids. However, as Figure

4.16 shows, there is a significant difference between the spectra of CaA with Fe(II)

and those spectra without Fe(II). In these acidic conditions, Fe(II) and Fe(III) will be

present in equilibrium (Morgan and Lahav, 2007). The change in the profile of the

spectra is likely due to complexation between Fe(III) and CaA, as shown in the

spectra; similar to that obtained for aluminium-caffeic acid in aqueous acidic

solutions by Cornard and co-workers (2006). In fact, Hynes and O’Coinceanainn

(2004) have reported the formation of 1:1 complex between Fe(III) and CaA at pH

between 1.0 and 2.5 (Scheme 4.2). Moreover, previous studies have shown the

accelerated decomposition of H2O2 to •OH radicals by Fe(III) complexes of analogous

phenolic acids (Rivas et al., 2002).

OH

OH

OH

O

[Fe(H2O)5(OH)]2+ O

O

OH

O

Fe + H

Scheme 4.2

There is a shape drop in peak intensities at pH ≥ 5.0 for CaA and Fe(III)

mixtures (Figure 4.16b), likely to be associated with increased complex formation due

to increasing amounts of caffeate ions with pH rise. As the pKa1 of CaA is 4.38, there

is an increasing amount of deprotonation with increasing pH (Adams et al., 2002).

The drop in intensity may also be due to the removal of CaA by adsorbing onto the

iron precipitate formed under these pH conditions. The spectra of Figure 4.16b also

show that there was no change in the shape of the curves with increasing pH, so it is

probable that only one type of complex is formed between Fe(III) and CaA under the

conditions investigated.

119

Figure 4.16 Effect of pH (pH 4.0–6.0) on the absorption spectra of CaA

(0.055 mM) at 25 °C: (a) in the absence and (b) in the presence of

Fe(II) (0.04 mM).

0

5

10

15

20

25

200 250 300 350 400

Mo

lar A

bso

rp

tiv

ity

(L

mol–

1cm

–1)

(x 1

03)

Wavelength (nm)

pH 4.0

pH 4.5

pH 5.0

pH 5.5

pH 6.0

0

5

10

15

20

25

200 250 300 350 400

Mo

lar A

bso

rp

tiv

ity

(L

mol–

1cm

–1)

(x 1

03)

Wavelength (nm)

pH 4.0

pH 4.5

pH 5.0

pH 5.5

pH 6.0

(a)

(b)

120

The CaA mixtures were further characterised using ATR-FTIR spectroscopy.

From the FTIR data, a number of bands were used to monitor changes in CaA as a

result of the presence of Fe(II), and the presence of Fe(II) and sucrose. The spectral

bands of CaA and sucrose solutions, and CaA mixtures containing Fe(II) or Fe(II) and

sucrose are given in Table 4.13. Spectral bands were assigned based on literature data

for CaA (Sánchéz-Cortés and García-Ramos, 1999; Dürüst et al., 2001; Machado et

al., 2009; Świsłocka, 2013), similar phenolic acids (Dobson and McQuillan, 2000;

Hanna and Quilès, 2011; Kalinowska et al., 2011; Świsłocka et al., 2012) and sucrose

(Vasko et al., 1971; Huvenne et al., 1981; Kodad et al., 1994; Kačuráková and

Mathlouthi, 1996; Max and Chapados, 2001). Bands attributable to aromatic ring

vibrations are numbered using the Wilson notation adapted by Varsányi (1974). The

main differences between the spectrum of CaA and that of Fe(II)–CaA are shown in

Figure 4.17. The ν(CC)ar aromatic bands (i.e., 8a and 19a) that occur at 1554 cm–1

and 1483 cm–1 (Świsłocka, 2013) are of increased intensity in the Fe(II)–CaA mixture

than that of CaA (Figure 4.17). The peak at ~1386 cm–1 associated with

ν(CC) + β(OH)ar (i.e., 14) (Świsłocka, 2013) is also of higher intensity in the spectrum

containing both Fe(II) and CaA. These increases in intensity may be attributed to

complex formation between the aromatic –OH group in CaA and Fe (III) (Hanna and

Quilès, 2011). The peak at 1275 cm–1 attributable to ν(C–OH) for CaA (Yost et al.,

1990; Machado et al., 2009) has shifted to a lower wavelength of 1265 cm–1 with

increase in intensity. This is a further confirmation of a strong association between

Fe(III) and CaA and that the complex formed is between Fe(III) and the phenolic

hydroxyl group (Rivas et al., 2002; Hynes and O'Coinceanainn, 2004). There was no

change in the band at 1672 cm–1 associated with ν(C=O) implying no evidence of

Fe(III) bonding to the carboxylic acid group of CaA. Previous works have shown that

with other phenolic acids, linkages are formed with their carboxylic acid groups

(Hanna and Quilès, 2011; Kalinowska et al., 2011; Świsłocka et al., 2012).

121

Table 4.13 Wavenumbers (cm–1) of selected bands from ATR-FTIR spectra of

CaA solution and CaA mixtures containing Fe(II) and/or sucrose

at pH 5.5 and 25 °C.

CaA mixtures

CaA Fe(II) Fe(II)/Sucrose Sucrose Band assignments*

3401 3495 ν(OH)

3274 3247 ν(OH)ar

3182 3113 ν(OH)

2981 2981 2980 2980 ν(CH)C=C + ν(CH)

2921 2933 2933 ν(CH) 20a

2900 2900 ν(CH)

2854 2852 ν(OH)

1672 1672 1669 1674 ν(C=O)

1618 1608 1611 1619 ν(CC)C=C

1554 1550 1567 1578 ν(CC)ar 8a

1524 1524 ν(CC)ar 8b

1483 1483 ν(CC)ar 19a

1454 1454 1454 1454 ν(CC)ar 19b

1426 1426 β(COH)

1386 1388 1377 1377 ν(CC) + β(OH)ar 14

1328 1329 1332 1332 β(CH)C=C

1275 1265 1274 1266 ν(C–OH)

1210 1210 β(CH)

1160 1160 β(CH) 18a

1118 1118 β(CH) 18b

1085 1085 β(OH)

1045 1045 1045 1045 γ(CH)C=C + γ(CH) 17b

1018 1018 ν(C–O)

998 998 β(COH)

927 927 ν(CC)

877 877 876 876

830 830 β(CCH)

122

Figure 4.17 Normalised ATR-FTIR spectra of CaA solutions at 25 °C after

subtraction of acetate buffer (pH 5.5): (a) in the absence and (b) in

the presence of Fe(II).

The spectrum for CaA, Fe (II) and sucrose (Figure 4.18) show that the broad

band that occurs at 3495 cm–1 ν(OH) (Max and Chapados, 2001) which is associated

with sucrose has shifted by 94 cm–1 to a lower wavenumber of 3401 cm–1. This

implies hydrogen-bonding interactions between CaA, Fe(III) and sucrose and could

well explain why CaA degradation increases with increasing sucrose concentration

(Gilfillan et al., 2012). These interactions provide supporting evidence of the

differences in the degradation behaviour of CaA and the other two HCAs (viz., pCoA

and FeA).

80010001200140016001800

Ab

sorb

an

ce (

Arb

itra

ry U

nit

s)

Wavenumber (cm–1)

1483

cm–

1

1386

cm–

1

15

54

cm–

1

16

72

cm

–1

12

65

cm–

1

(a)

(b)

123

Figure 4.18 Normalised ATR-FTIR spectra of CaA solutions containing

sucrose at 25 °C after subtraction of acetate buffer (pH 5.5): (a) in

the absence and (b) in the presence of Fe(II).

Response Surface Analysis

Graphical representations of the regression model in the form of 3D surface

plots were used to provide a pictorial view of the interactions between the

independent variables on total HCA degradation. These plots are shown in Figure

4.19, where two independent variables were varied within the experimental ranges

investigated while the remaining variables were kept constant. The interactions are

significant as the curvature of the surfaces is obvious.

24002800320036004000

Ab

sorb

an

ce (

Arb

itra

ry U

nit

s)

Wavenumber (cm-1)

34

95

cm

–1

34

01

cm

–1

Δν = 94 cm–1

(a)

(b)

124

Figure 4.19 Three-dimensional surface plots of total HCA degradation (%) as

a function of (a) total HCA and sucrose; (b) sucrose and pH; and

(c) pH and temperature. Variables: total HCA (155 mg/L);

sucrose (7.5% (w/w)); pH (5.0) and temperature (35 °C).

Design-Expert® SoftwareOriginal Scale(% Total HCA Degradation) 3̂

76.7742

49.6923

X1 = A: Total HCAX2 = B: Sucrose

Actual FactorsC: pH = 5.00D: Temperature = 35.00

65

88

110

133

155

4

6

8

9

11

60

63

66

69

72

%

Tota

l HC

A D

egra

datio

n

A: Total HCA B: Sucrose


76.7742

49.6923

X1 = B: SucroseX2 = C: pH

Actual FactorsA: Total HCA = 155D: Temperature = 35.00

4

6

8

9

11 4.75

4.88

5.00

5.13

5.25

59

61

63

65

67

%

Tota

l HC

A D

egra

datio

n

B: Sucrose C: pH


76.7742

49.6923

X1 = C: pHX2 = D: Temp.

Actual FactorsA: Total HCA = 155.00B: Sucrose = 7.50

4.75

4.88

5.00

5.13

5.25 31

34

38

41

44

60

61

62

63

65

%

Tota

l HC

A D

egra

datio

n

C: pH D: Temp.

(a)

(b)

(c)

125

The variables of sucrose concentration and initial total HCA concentration

were varied as shown in Figure 4.19a, whilst the other variables, namely pH and

temperature were kept constant at 5.0 and 35 °C respectively. These fixed values

were chosen as they were similar to that typical of process sugar cane juice (Nguyen

and Doherty, 2012). The total HCA degradation efficiency decreases with increasing

sucrose concentration and the initial total HCA concentration. Increasing the initial

total HCA concentration did not significantly decrease the degradation efficiency of

the HCAs. This can be seen by both the coefficient of the first- and second-order

model term (Equation 4.9) for total HCA concentration (i.e., A) and in Figure 4.19a

where there was only a 5.9% discrepancy between 65 mg/L and 155 mg/L of initial

total HCA at 3.75% (w/w) sucrose. This discrepancy is not noticeable at higher

sucrose concentrations. It can be said from these observations, that the optimal

Fenton dosage is capable of degrading higher concentrations of HCAs and other

components (similar to that of HCAs) than at the highest concentration studied (i.e., >

200 mg/L).

Sucrose concentration showed a significant effect on the degradation of the

HCAs (Figure 4.19b). Degradation increases smoothly with an increase in pH from

4.75 to 5.0 but decreases gradually when the pH exceeds 5.0, at any given

concentration of sucrose. The negative effect on total HCA degradation at lower pH

than the optimal may be attributed to the scavenging effect of H+ or •OH radicals

which can inhibit the reduction of Fe(III) to Fe(II) and prevent the further generation

of •OH radicals (Rivas et al., 2005; Deng, 2007). On the other hand, the negative

effect at pH above the optimal may be attributable to the deactivation of the Fe(II)

catalyst with the formation of Fe(III) oxyhydroxide in lieu of being regenerated back

to Fe(II) (Bigda, 1995). The formation of Fe(III) oxyhydroxides in the present study

was confirmed by analysing the precipitates obtained at pH 5.5 and 25 °C, by XRD

(Table 4.14). The d-spacing values 6.21 Å, 3.28 Å, 2.46 Å and 2.36 Å correspond to

lepidocrocite (i.e., Fe(III) oxide hydroxide), FeO(OH), while the peaks at 5.20 Å and

2.04 Å is associated with CaA (Dong et al., 2012). The formation of oxyhydroxide is

derived from the following reaction equation (Equation 4.10):

Fe2+ + ¼O2 + 2OH– FeOOH + ½H2O (4.10)

126

Table 4.14 X-ray diffraction data of the precipitate formed between CaA and

Fe(II) at pH 5.5 and 25 °C.

d-spacing (Å)

Intensity (counts) Precipitate FeO(OH)*

126.31 6.2164 6.2580

80.550 5.2003

204.48 3.2821 3.2933

161.52 2.4644 2.4737

138.89 2.3609 2.3635

543.59 2.0409

78.600 1.9320 1.9365

36.960 1.7282 1.7350

28.340 1.5252 1.5360

112.57 1.2941 1.2990

*Based on a FeO(OH) reference pattern (ICDD PDF card 04-010-4300).

The reduction in degradation effectiveness at pH above the optimal, in

addition could be because H2O2 is relatively unstable and may rapidly decompose to

H2O and O2 (Kuo, 1992; Chang et al., 2010). Thus at pH below or above the optimal,

the amounts of Fe(II) and/or H2O2 required to catalyse the Fenton oxidation process is

reduced.

Figure 4.19c shows the interaction effects of pH and temperature on HCA

degradation. The non-significance of the temperature variable is evident by the

narrow range on the response axis (i.e., 57–64%). Despite this, the degradation trend

on the HCAs in terms of temperature is still observable. Increasing temperature leads

to less degradation of the HCAs. The decomposition of H2O2 by Fe(II) is not directly

linked to the amount of HCA degraded. In addition to the formation of •OH radicals

by the Fenton process, non-reactive species such as H2O and O2 are also formed at

higher temperatures (> 40 °C) (Rodrigues et al., 2009b). The Fenton process was the

only contributor to the degradation of HCAs as there was no thermal decomposition

of any of the HCAs within the temperature range studied (25–50 °C).

127

Process Optimisation and Validation

Numerical optimisation was performed on the basis of the desirability function

to determine the optimum process parameters for the degradation of the HCAs. The

desirability function is expressed numerically from a scale of 0 to 1 (lowest to highest

desirability) and denotes the degree of importance in obtaining the desired response

value (Harrington, 1965). A desirability function value can be constructed by using

five different goal optimisation constraints: none, maximum, minimum, target and

within range. On the basis of the fitted quadratic models, an optimised response value

can then be predicted by using the chosen goal optimisation criteria that maximises

the desirability function. In order to simultaneously optimise numerous responses

(i.e., multi-response optimisation), the desirability function values for each response

(i.e., CaA, pCoA and FeA) are combined into an overall desirability function by

computing their geometric mean of different desirability values, as shown in Equation

4.11 (Derringer and Suich, 1980).

11

1 2 3 1

1

( ... )n n

nn

i

D d d d d d

(4.11)

where, D overall desirability function

di desirability of the response

n number of responses investigated

In order to confirm the accuracy and robustness of the predicted models and

assess its reliability to predict the (%) degradation of HCAs, additional experiments

were carried out under those conditions, as well as selected conditions of process

streams close to that of a typical Australian sugar mill.

For this study, the desirability functions for the three individual HCA

degradation models were combined into one value and compared to the desirability

function of the total HCA model (Table 4.15). The combined desirability function

values of the three individual HCA models for the experiments were relatively close

to the desirability values produced for the single total HCA degradation model. This

128

indicates that there is little variation between the simultaneously predicted values of

each HCA degraded and the predicted value for the total HCA degraded.


degradation of total HCA (200 mg/L) and model verification.*

Experiments

Water Worst

case

Synthetic

juice 1

Synthetic

juice 2

Sucrose (% (w/w)) 0 14.00 13.00 21.00

pH 4.7 4.5 5.4 4.9

Temperature (°C) 25 40 36 30

CaA degradation (%) 92 (90) 87 (90) 73 (72) 78 (68)

pCoA degradation (%) 69 (68) 33 (37) 48 (52) 52 (75)

FeA degradation (%) 70 (64) 40 (46) 51 (56) 54 (84)

Total HCA degradation (%) 77 (73) 53 (49) 57 (58) 61 (67)

Desirability

Combined models 0.720 0.542 0.383 0.655

Total HCA degradation model 0.743 0.632 0.332 0.621

*Values in parentheses indicate model predicted % degradation for each

individual/total HCA model. Measurements were conducted in triplicate.

RSD was < 5.0%.

As shown in Table 4.14 the experimental and predicted values (in parentheses)

for the degradation of each and the total of the HCAs, under specified constraints.

The optimum conditions for maximum degradation of HCAs (200 mg/L) using the

Fenton process (0.5 mM Fe(II) and 7.5 mM H2O2) are 0% (w/w) sucrose

(i.e., aqueous), pH 4.7 and 25 °C. Under these conditions 92% CaA, 69% pCoA and

70% FeA was degraded (i.e., total HCA degradation of 77%). The experimental

values of the optimum conditions agree well with the predicted values deduced from

each of the four models. The low error in the experimental and predicted values

indicates good agreement of the results. The experimental values obtained for the

129

worst conditions were also in good agreement with the predicted values. The good

agreement between values is attributable to the high combined desirability function

value. It is worth mentioning that the sum of the predicted degradation values of the

individual HCA degradation models (i.e., CaA, pCoA and FeA) is not equal to the

predicted total HCA degradation values. Hence, the individual degradation models

should be only used as a guide to predict the degradation of the total HCAs present in

a mixture.

The best results for the synthetic juices are obtained with solutions having

similar sucrose content and operating temperature as factory MJ (synthetic juice 1)

followed by factory No. 1 mill juice (i.e., juice expressed from the first mill of a

quintuple set of mills) (synthetic juice 2). Despite a low desirability function value

predicted under the synthetic juice 1 conditions, the experimental results were in close

agreement with the predicted values for all four models. The lower desirability may

be due to some of the constraints that were not close to any of the design points of the

CCD. On the other hand, a higher error was observed for synthetic juice 2, despite a

reasonable desirability value. In addition, the experimental values obtained for pCoA

and FeA degradation were significantly lower than the predicted values. It is highly

probable therefore, that the presence of sucrose may have contributed to the

inaccuracy of this prediction as its concentration was outside the range used to

develop the proposed models. Therefore, it is not recommended to use constraints

outside the ranges studied for multi-response optimisation, as the responses are all

dependent on each other.

From these results, the Fenton process can successfully be used to degrade

HCAs (i.e., colour precursor compounds) under the operating conditions in a raw

sugar manufacturing factory.

130

4.4 Summary

In this chapter, the experimental procedures and statistical treatment of

experimental results for the degradation of three phenolic acids (CaA, pCoA, and

FeA) is described in great detail. For each selected acid there are up to seven

independent variables considered, with conditions covering the normal ranges

experienced in sugar cane juice processing and about 28 combinations of these are

examined for significance in a quadratic regression to determine an expression for the

% degradation. The statistical manipulations to obtain a meaningful expression for

degradation are given in detail. The effect of variations in individual parameters was

sometimes rationalised by appealing to their effect on the availability of free •OH

radicals for degrading the particular phenolic acid being studied.

The degradation of HCA mixtures by the Fenton process has been studied in

water and sucrose solutions. From the information obtained from the initial model for

the degradation of CaA; four additional quadratic models were developed and showed

the working relationship between the degradation efficiency of each HCA (i.e., CaA,

pCoA and FeA) with four independent variables (i.e., initial HCA mixture

concentration, sucrose concentration, solution pH and reaction temperature). Under

the optimised conditions for a 200 mg/L initial HCA mixture concentration, the

degradation efficiencies of the mixture in water and sugar solutions (i.e., 13% (w/w))

were 77% and 57% respectively.

The behaviour of CaA degradation in the composite system is different from

that of pCoA and FeA possibly because of its ability to form complexes with Fe(III),

as its aromatic ring is highly activated with the presence of two hydroxyl groups. In

addition, CaA has been shown to hydrogen-bond with sucrose – a free radical

scavenger.

The Fenton process has been shown to degrade HCAs in sucrose solutions

with minimum sucrose breakdown. This means that the process may find use in the

raw sugar manufacturing process for the removal of these and other colour precursors

that are significantly prevalent when the juice expressed from the whole sugar cane

biomass (instead of the stalk) is processed. As the sugar cane industry around the

world is looking towards diversification by value-adding with the excess biomass

produced from whole crop processing, the use of the Fenton or similar processes will

131

allow juice expressed from the whole sugarcane plant to be cost-effectively processed.

The advantages of the use of the Fenton process in the sugar manufacturing process

include its simplicity, its non-specific oxidation property and the use of inexpensive

equipment. Also, the sludge that is produced has the potential to remove colourants

and other impurities (including proteins and polysaccharides) improving the quality of

the juice feedstock.

132

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136

137

CHAPTER 5

Separation and Identification of

Fenton Oxidation Products

Derived from Hydroxycinnamic Acids

5.1 Introduction..................................................................................... 137




Hydroxycinnamic Acid Mixtures.........................................

138

5.2.3 Sample Preparation............................................................. 139



Sucrose Mixtures.................................................................

142

5.2.6 Computational Methods...................................................... 142


5.3.1 Identification of Oxidation Products................................... 143

5.3.2 Proposed Degradation Pathways of Selected

Hydroxycinnamic Acids.......................................................

153

5.3.3 Oligomerisation of Hydroxycinnamic Acids....................... 166

5.4 Summary.......................................................................................... 171

138

5.1 Introduction

In this chapter, attempts were made to determine the oxidation products and

the degradation pathways of the HCAs studied (viz., CaA, pCoA and FeA). To date,

inadequate information exists in relation to the oxidation of these compounds using

the Fenton process and other established AOPs. Not all of the oxidation products of

these HCAs have been previously isolated or identified. The identification of these

products will assist in proposing possible mechanistic pathways of the oxidation

process. Therefore, the investigations in this chapter are further attempts to identify

these products and propose probable mechanisms for the degradation of these

phenolic acids.



All chemicals, solvents and reagents were obtained in their purest form from

the suppliers as described in the previous chapters or as otherwise stated. Acetic acid,

cis–aconitic acid, trans–aconitic acid, butyric acid, citric acid, formic acid, fumaric

acid, glycolic acid, glyoxylic acid, isobutyric acid, lactic acid, malic acid, oxalic acid

and succinic acid were purchased from Sigma-Aldrich (St. Louis, MO, USA).

Potassium chloride was obtained from Ajax Finechem (Seven Hills, NSW, Australia).

Stock solutions of HCAs (i.e., CaA, pCoA and FeA) were prepared by dissolution in

degassed ethanol solution (50% (v/v)) and stored at 4.0 °C.

5.2.2 Fenton Oxidation Reactions for the Degradation of Hydroxycinnamic

Acid Mixtures

The procedure for the Fenton oxidative degradation of HCA mixtures is

similar to that described in Section 4.2.4. Four HCA solutions were investigated

(viz., CaA, pCoA, FeA and their mixture) under the optimised operating conditions in

water, pH 4.7 and 25 °C (cf. Section 4.3.3).

139

In each run, a predetermined amount of Milli-Q water and each HCA were

added to the reaction vessel. To improve the analytical detector response of the

reaction products, the starting materials for the combined HCA mixture were carried

out at two orders of magnitude higher than those studied in Section 4.3.3. The final

concentrations for each HCA component were CaA (37 mM), pCoA (41 mM) and

FeA (34 mM). The total HCA concentration is approximately equivalent to

20,000 mg/L (i.e., 100× more than 200 mg/L used for total HCA concentration in

Chapter 4). For the three individual HCA mixtures, an initial HCA concentration of

100 mM (approximately equivalent to 20,000 mg/L) was added to the reaction vessel.

Known amounts of FeSO4·7H2O (0.5 M, 2.49 mL) and H2O2 (5.0 M, 0.75 mL)

solutions were added to achieve a final volume of 50 mL and a final concentration of

24.9 mM and 75 mM, respectively. The working molar ratio (Fe(II)/H2O2) for the

Fenton reaction was 1:15. The reaction was initiated as soon as H2O2 was added. At

2 min, 3.0 mL of the solution was taken, diluted 10-fold to quench the reaction and

stored at 4.0 °C. The remainder of the mixtures were immediately snap-frozen in

liquid nitrogen and stored at –80 °C. Samples were defrosted and prepared for

instrumental analysis.

5.2.3 Sample Preparation

Undiluted samples were pre-concentrated using solid phase extraction (SPE)

prior to gas chromatography/electron impact-mass spectrometry (GC/EI-MS)

analysis. Undiluted samples for high-performance ion exclusion chromatography

(HPIEC) analysis did not require any sample preparation. Diluted samples for the

analyses on all other analytical techniques required no further sample preparation.

Samples for GC/EI-MS analyses did not require any further adjustment prior

to SPE. Waters Sep-Pak tC18 vacuum cartridges (3 cc, 500 mg, 37-55 µm)

(Wexford, Ireland) were placed in a Waters Sep-Pak 24-port vacuum manifold

(Milford, MA, USA) and first conditioned with 2 × 2.5 mL HPLC grade methanol

followed by 2 × 2.5 mL of Milli-Q water. After the conditioning step, 2 × 1.0 mL

aliquots of the reaction sample were loaded at a flow rate lesser than 2.0 mL/min by

adjusting the vacuum to ca. 15 kPa. The column was washed with 2.5 mL of Milli-Q

140

water. Finally, elution was performed with 2 × 1.0 mL HPLC grade methanol at a

flow rate ≤ 1.0 mL/min by adjusting the vacuum to ca. 10 kPa. The eluates obtained

were concentrated by solvent evaporation under a gentle stream of N2 and

recomposed to a final volume of 1 mL in HPLC grade methanol. The extracts were

membrane filtered (0.45 μm) prior to GC/EI-MS analysis.


HPLC-DAD/ESI-Q-TOF-MS/MS. Identification of organic reaction products

was evaluated using reversed-phase HPLC coupled with UV/Vis DAD and

electrospray ionisation quadrupole time-of-flight tandem mass spectrometry (ESI-Q-

TOF-MS/MS). Analyses were performed on an Agilent 1290 Infinity LC system

(G4220A binary pump, Germany; G4226A ALS, Germany; G1330B ALS thermostat,

Germany; G1316C thermostatted column compartment, Germany; G1314E variable

wavelength detector (VWD), Germany) coupled with an Agilent Accurate-Mass Q-

TOF mass spectrometer (G6520B, USA). The chromatographic conditions were

identical as described in Section 4.2.5 for all samples with the exception of single

wavelength UV/Vis detection (280 nm) and injection volume (20 μL). The column

effluent from the VWD was then introduced into the dual ESI source of the Q-TOF

mass spectrometer without post-column splitting. Mass spectra were acquired in

negative ion mode and the conditions were set as follows: gas temperature: 350 °C;

drying gas flow (N2): 12 L/h; nebuliser (N2): 35 psig; capillary voltage: 3.5 kV;

fragmentor: 170 V; skimmer: 60 V; OCT1 RF Vpp: 250 V. Data acquisition was

performed using the Agilent Masshunter Data Acquisition TOP/Q-TOF B.02.00

software package, scanning from a mass-to-charge ratio (m/z) 100 to 1500 in profile

(continuum) mode with a scan cycle time of 2.242 s and an acquisition time of 714.1

ms/spectrum. Two reference masses (m/z 121.050873 and m/z 922.009798) were

used. Tandem MS product ions were produced by collision-induced dissociation of

selected precursor ions in the collision cell of the Q-TOF mass spectrometer at fixed

collision energy voltage of 50 V. The Agilent Masshunter Qualitative Analysis

(B.02.00) software package was used for data analysis.

141

HPIEC. Identification of carboxylic acids was evaluated using high-

performance ion exclusion chromatography (HPIEC). Analyses were performed on a

Waters HPLC system (Milford, MA, USA) equipped with a 626 pump, a 600S

controller, a 717plus autosampler, a 2487 dual λ absorbance detector and a 410

differential refractometer. The separation was carried out on two Bio-Rad Aminex

HPX-87H ion exclusion columns (300 × 7.8 mm i.d.) (Heracles, CA, USA) connected

in series and protected by a Bio-Rad Cation H+ micro-guard cartridge (30 × 4.6 mm

i.d.). Reaction products were eluted isocratically with 8 mM H2SO4 (sparged with

helium at 10 mL/min). Simultaneous UV/Vis detection at specific wavelengths (190

nm and 210 nm) without reference wavelength subtraction. Refractive index

detection was set to negative polarity and run at an attenuator setting of 20×. Aliquots

of samples were membrane filtered (0.45 μm) prior to injection into the HPLC

system. Injection volume for all samples was 20 μL; first and second column

temperatures were equilibrated at 35 °C and 85 °C respectively; flow rate was 0.4

mL/min and run time was 90 min. Data acquisition was performed using the Waters

Empower 2 (Build 2154) software package. Identification of peaks was based on the

conformance of UV/Vis spectra and retention times with the corresponding authentic

standards.

GC/EI-MS. Identification of volatile reaction products was evaluated using

GC/EI-MS. Analyses were performed on a Hewlett Packard HP/Agilent 6890, 7683

and 5973 Series GC/MS system (G1530A (6890A) gas chromatograph system, USA;

G2614A (7683) ALS Tray, China; G2613A (7683) ALS Injector, China; G1926A

(7683) Bar Code Reader, China; G2589A (5973N) Mass Selective Detector; USA)

using a Phenomenex Zebron ZB-1 GC capillary column (30 m, 0.25 mm i.d. and 0.25

μm thick film) (Torrance, CA, USA). Helium was used as carrier gas at a constant

flow rate of 0.5 mL/min. Sample aliquots (3.0 μL) were injected in splitless mode at

an injector temperature of 250 °C. The oven temperature program was 4 min at 40

°C; 8 °C/min to 180 °C (2 min); and 8 °C/min to 280 °C (9 min). The mass range

scanned was 35 m/z to 500 m/z using EI ionisation at 70 eV. Data acquisition and

analysis was performed using the Agilent MSD ChemStation (G1701EA E.01.00.237)

software package.

142

5.2.5 Fenton Oxidation Reactions for the Degradation of Sucrose Mixtures

Four 50 mL mixtures containing only sucrose at various concentrations

(3.75%, 7.50%, 11.25% and 15.0% (w/w)) were subjected to Fenton oxidation under

the optimum operating conditions described in Section 4.2.4 (i.e., 2.49 mM

FeSO4·7H2O, 7.50 mM H2O2, pH 5.4 and 36 °C). These experiments were carried out

to determine the presence of organic acids and reducing sugars formed from the

Fenton oxidation of sucrose. At 2 min, the mixtures were immediately snap-frozen in

liquid nitrogen and stored at –80 °C. Samples were defrosted and prepared for

HPIEC and HPAEC-PAD analyses for the determination of organic acids and

reducing sugars, respectively.

Prior to HPIEC analysis, samples containing sugars were adjusted to pH 8.5

using 0.1 M NaOH prior to SPE to facilitate the ion exchange in the packed cartridge.

Waters AccellPlus QMA vacuum cartridges (3 cc, 500 mg, 37-55 µm) (Wexford,

Ireland) were placed in a Waters Sep-Pak 24-port vacuum manifold and first

conditioned with 2 × 2.5 mL of 0.5 M potassium chloride solution followed by

2 × 2.5 mL of Milli-Q water. After the conditioning step, 2 × 2.0 mL aliquots of the

reaction sample were loaded at a flow rate ≤ 1.0 mL/min by adjusting the vacuum to

ca. 5 kPa. The column was washed with 8 × 2.5 mL of Milli-Q water. Finally,

elution was performed with 2 × 2.0 mL of 0.1 M sulfuric acid at a flow rate ≤ 0.5

mL/min by adjusting the vacuum to ≤ 5 kPa. The extracts were membrane filtered

(0.45 μm) prior to HPIEC analysis. The operating procedure for the chromatographic

system is identical to that described in Section 5.2.3.

Sucrose and reducing sugar contents in the reaction mixtures were monitored

by HPAEC-PAD. Sample preparation and the operating procedure for the

chromatographic system are identical to that described in Section 4.2.5.

5.2.6 Computational Methods

Geometry optimisations of HCA molecular systems in their ground state were

performed using the density functional theory (DFT) methods implemented using the

Wavefunction, Inc. Spartan ′10 (1.1.0) software package (Irvine, CA, USA). Density

functional theory was chosen as the method for computation as it provides a

143

reasonable description of the electronic correlation of a molecule in a quantum system

within minimal computational time and cost (Fifen et al., 2009). Also, the precision

of the DFT is typically better than that of other methods (e.g., Hartree-Fock (HF),

semi-empirical) where the electron spin is not considered (Nsangou et al., 2008).

Density functional approximations were calculated based on the B3LYP hybrid

functional, which consists of the Becke’s three parameters exact exchange functional

(B3) (Becke, 1988) combined with the non-local gradient corrected correlation

functional of Lee-Yang-Parr (LYP) (Lee et al., 1988). The standard split valence

double-zeta Gaussian basis set 6-31G augmented by a set of d polarisation functions

(Frisch et al., 1984) on heavy atoms was chosen. Solvent effects of water are

computed in the framework of a restricted HF-DFT self-consistent field SM8 model

using the Pulay direct inversion iterative subspace approach (Pulay, 1980) with

geometric direct minimisation (Van Voorhis and Head-Gordon, 2002). The outputs

produced from the theoretical calculations are presented in Appendices, Tables

A2.1–A2.3.


5.3.1 Identification of Oxidation Products

In comparison to other AOPs (e.g., ozonation, UV/H2O2 oxidation), the

mechanism of the Fenton process between Fe(II) and H2O2 is already intricate and is

further complicated when an organic compound is involved in the reaction. So,

mechanistic pathways for the degradation of organic compounds using the Fenton

process have only been proposed for simpler compounds. Structural elucidation and

proposal of reaction schemes is complex in the case of HCAs, since these phenolic

derivatives are molecularly larger and have more available sites for free radical attack.

In turn, they may produce complex intermediates or produce several smaller products

at lower concentrations that are difficult to detect. Identification of these products

will assist in proposing possible mechanistic pathways for the oxidative degradation

of the HCAs investigated.

144

Liquid Chromatography Techniques

The stoichiometry for the complete mineralisation of CaA (C9H8O4), pCoA

(C9H8O3) and FeA (C10H10O4) by H2O2 in the Fenton process is as follows:

C9H8O4 + 18H2O2 9CO2 + 22H2O (5.1)

C9H8O3 + 19H2O2 9CO2 + 23H2O (5.2)

C10H10O4 + 21H2O2 10CO2 + 26H2O (5.3)

It is expected that the optimised working Fenton molar ratio (Fe(II)/H2O2) of

1:15 is insufficient for complete degradation of all acids, individually or combined.

The incomplete depletion of HCA peaks and the presence of new peaks detected as

depicted in the HPLC-DAD profiles of the reaction mixtures (Figure 5.1) suggest that

there are reaction products remaining in solution. The identification of these

compounds is important in order to assess and predict their role in downstream

processes of the sugar manufacturing process. It is presumed that after 2 min, under

the optimum operating conditions in water or sucrose solutions, the reaction had

reached equilibrium as there were no changes in the response of the chromatographic

peaks of the starting compounds after 2 min of the reaction initiated by H2O2.

Figure 5.1 shows numerous chromatographic peaks corresponding to the

starting materials and the reaction products formed at 2 min of Fenton oxidation. The

numbers directly labelled on the peaks of the HPLC-DAD chromatograms (Figure

5.1) are associated with the identified products listed in Table 5.1. Products were

identified based on the comparison of retention time data of the available pure

standards and/or accurate mass measurements. Proposal of chemical structures were

evaluated based on the predicted oxidation mechanism of the Fenton process and

mass spectral fragmentation patterns of similar compounds suggested in the literature

(Fulcrand et al., 1994; Antolovich et al., 2004). Additional information was obtained

from the isotopic distribution in the mass spectra for certain molecules. Table 5.1 also

shows data related to the experimental and calculated masses of the deprotonated ions

and proposed empirical formulae related to the identified compounds. The resulting

accurate masses were found with an error lower than 0.04 Da.

145

Figure 5.1 High-performance LC-DAD chromatograms (UV/Vis detection at

280 nm) of (a) CaA; (b) pCoA and (c) FeA; subjected to Fenton

oxidation at 2 min (pH 4.7, 25 °C). Numbers correspond to

compound numbers in Table 5.1.

-10

30

70

110

150

0 5 10 15 20 25

Ab

sorb

an

ce (

mA

U)


1

2

3

-10

30

70

110

150

0 5 10 15 20 25

Ab

sorb

an

ce (

mA

U)


4 5

-10

30

70

110

150

0 5 10 15 20 25

Ab

sorb

an

ce (

mA

U)


6

7

8

(a)

(b)

(c)

146


detected by LC/MS.

Peak Compound tR

(min)

Molecular ion

(m/z)

Formula Error

(Da)

Caffeic acid

1 protocatechuic aldehyde 4.24 137.048 C7H5O3– –0.02

2 caffeic acid 7.02 179.061 C9H7O4– –0.03

3 caffeic acid tetramer 12.75 715.183

p–Coumaric acid

4 4–hydroxybenzaldehyde 6.83 121.050 C7H5O2– –0.02

5 p–coumaric acid 10.64 163.065 C9H7O3– –0.03

Ferulic acid

6 vanillin 7.12 151.065 C8H7O3– –0.03

7 ferulic acid 11.60 193.079 C10H9O4– –0.03

8 ferulic acid dimer 18.87 385.133 C20H17O8– –0.04

The strong intense peak at the retention time (tR) range of 0.83–0.98 min in the

HPLC-DAD and is attributable to the solvent. Five products were identified by

means of reversed-phase HPLC with UV/Vis DAD and negative ion mode ESI-MS

detection and had retention times of less than 20 min. The chromatogram obtained

for the combined HCA mixture (cf. Appendices, Figure A2.1) revealed no new peaks

than those that already appear in the individual HCA mixtures in Figure 5.1. This

may suggest that there were no side reactions taken place among the HCAs.

It is observed that at 2 min, the Fenton oxidation of CaA produced two main

products observable at a wavelength of 280 nm. Protocatechuic aldehyde

(3,4–dihydroxybenzaldehyde) was assigned to the eluted peak at tR = 4.24 min. This

product is the initial breakdown product of CaA as a result of •OH radical attack to

the vinyl functional group of the phenolic acid. The later oxidation product had a m/z

ion of 715 which was tentatively assigned to the tetramer of CaA (i.e., [M4–H]–). It is

assumed that the tetramer could have possibly been formed by the oxidative coupling

of two dimers of CaA (MW of 358) (i.e., [M2–H]–) (Pati et al., 2006). Unlike

previous reports, the dimers formed by the Fenton oxidation of CaA were not

147

observed in this study (Cilliers and Singleton, 1991; Tazaki et al., 2001; Antolovich et

al., 2004).

The main product observed from the Fenton oxidation of pCoA is

4–hydroxybenzaldehyde (tR = 6.83 min). There were several other unidentified peaks

with intensities lower than 4–hydroxybenzaldehyde. Similar to CaA, only two main

oxidation products were produced from the oxidation of FeA, they are vanillin

(4–hydroxyl–3–methoxybenzaldehyde) at tR = 7.12 min and a dimer of FeA

(i.e., [M2–H]–) at tR = 18.87 min. The dimeric products detected are in consistency

with those found in similar oxidation studies (Antolovich et al., 2004; Šmejkalová et

al., 2006).

In addition to the HPLC-DAD chromatograms shown in Figure 5.1 for the

individual HCA solutions, the total ion chromatograms (TICs) recorded in negative

ion mode ESI-MS for each phenolic acid mixture is shown in Figure 5.2. The mass

spectral fragmentation pattern data extracted from peaks obtained in the TICs were

similar to those extracted from the HPLC-DAD chromatographic peaks. Hence, the

results shown in the TICs are consistent with the results obtained with DAD.

The peak at tR = 6.15 min shown in Figures 5.1b and 5.2b has a m/z of 121 and

the peak at tR = 6.83 min also has the same m/z. The peak at tR = 6.83 min has been

reported previously to be attributable to 4–hydroxybenzaldehyde. Therefore, the peak

at tR = 6.15 min may be an isomer formed as a result of hydroxylation during

(Poerschmann et al., 2010). Three unidentified peaks at tR = 17.7, 19.7 and 21.3 min

observed in each of the TICs shown in Figure 5.2 are possibly due to impurities

present in the chromatographic system and are not associated to the starting materials

and its reaction products.

148

Figure 5.2 Total ion chromatograms (negative ion mode ESI-MS) of (a) CaA;

(b) pCoA and (c) FeA; subjected to Fenton oxidation at 2 min

(pH 4.7, 25 °C). Numbers correspond to compound numbers in

Table 5.1.

0

5

10

15

20

25

0 5 10 15 20 25

Inte

nsi

ty (

x1

06)


12

3

0

5

10

15

20

25

0 5 10 15 20 25

Inte

nsi

ty (

x1

06)


4

5

0

5

10

15

20

25

0 5 10 15 20 25

Inte

nsi

ty (

x 1

06)


67

8

(a)

(b)

(c)

149

Ion Chromatography Techniques

Despite oligomer formation, the presences of the phenolic aldehydes produced

from the Fenton oxidation of the HCA mixtures show that the Fenton process is

decomposing the HCAs into smaller products. Based on the oxidative degradation

mechanisms of aromatic compounds by the Fenton process, proposed by Neyens and

Baeyens (2003), it is expected that the phenolic aldehydes would undergo further

oxidation via hydroxylation causing the aromatic rings to open and form LMW

aliphatic carboxylic acids. Hence, HPIEC was used in this project to determine the

presence of carboxylic acids.

Between the two HPIEC detection methods used (viz., UV/Vis and RI),

improved baseline resolution and peak separation was achieved with RI detection.

Problems associated with UV/Vis detection at 190 nm and 210 nm wavelengths

include broadening and overlapping of chromatographic peaks that may be

attributable to other reaction intermediates and products that strongly or partially

absorb at the chosen wavelengths. In the present study, butyric, cis–aconitic, formic,

acetic, glyoxylic, isobutyric, lactic, and oxalic acids were detected in each of the

reaction mixtures (Table 5.2).

Table 5.2 Contents of organic acids (mM) by HPIEC of individual and

combined HCA mixtures.*

CaA pCoA FeA Mixture

cis–Aconitic 0.016 0.015 0.016 0.015

Butyric 0.065 0.054 0.044 0.061

Formic 0.16 0.39 0.45 0.36

Glyoxylic 0.21 0.19 0.19 0.19

Isobutyric 5.0 2.6 3.9 3.6

Lactic 0.0038 0.0076 0.0026 0.003

Oxalic 5.2 4.8 4.7 4.6


150

However, these compounds except for oxalic and isobutyric acids were found

at low concentrations (≤ 0.45 mM). This may indicate that progressive oxidative

degradation from HCA is minimal under the operating conditions; or that these

carboxylic acids may have decomposed to CO2 and H2O within 2 min of the reaction.

Oxalic acid and isobuytic acid concentrations in each of the three individual acids

ranged from 4.7–5.2 mM and 2.6–5.0 mM, respectively. The highest concentrations

of oxalic, isobutyric and glyoxylic acids were obtained with CaA. However, higher

amounts of butyric and formic acids were produced from pCoA and FeA degradation.

Interestingly, these organic acids are typically organic acids found in sugar cane juice

(Thai and Doherty, 2011).

Mixtures only containing Fenton’s reagent and sucrose at varying

concentrations (3.75%, 7.50%, 11.25% and 15.0% (w/w)) were examined by HPIEC

and HPAEC-PAD for the determination of carboxylic acids and reducing sugars,

respectively. With the HPIEC method, no peaks were observed in both UV/Vis and

RI chromatograms indicating that no carboxylic acids were produced from sucrose

degradation at 2 min of the reaction. However, the HPAEC-PAD analyses in fact

showed sucrose degradation (≤ 0.01%) and the presences of glucose and fructose

(≤ 0.02%) in the 3.75% and 7.50% (w/w) sucrose mixtures (cf. Appendices, Table

A2.4). It should be noted that the amount of sucrose degraded was not significant.

Gas Chromatography Techniques

The products produced from the Fenton oxidation of the individual acids and

their mixture were analysed by GC/EI-MS analysis. Gas chromatographic studies for

the monitoring of HCA degradation products have not been previously reported. This

may be due to the low volatilities of the products.

Figure 5.3 shows the gas chromatograms obtained for SPE extracts of each

HCA solution at 2 min of oxidation. The relatively smaller intensities and fewer

peaks on the GC chromatogram of degraded CaA (Figure 5.3a) show that CaA has

fewer volatile compounds than the other HCAs. The numbers directly labelled on the

peaks of the GC chromatograms shown in Figure 5.3 correspond to the identified

products listed in Table 5.3. These products have been identified based on their

151

molecular ion and mass fragmentation patterns. A compound identification program

of the National Institute of Standards and Technology library (Gaithersburg, MD,

USA) was also used to confirm these compounds with a fit value of ≥ 90% in all

cases.

The identification program matched several compounds with fitting values of

80–90% to peaks found in the extracts of pCoA and FeA reaction mixtures as shown

in Figures 5.3b and 5.3c, respectively. However, the structures of these matching

compounds were not strongly associated with any of the products with fit values of

≥ 90% and products detected by other techniques. Therefore, these compounds were

not considered for the proposal of mechanistic pathways for the degradation of HCAs.


detected by GC/MS.

Peak Compound tR

(min)

Formula EI/MS Spectrum Ions

(m/z)

Caffeic acid

8 p–vinylguaiacol 18.09 C9H10O2 150, 135, 107, 77

p–Coumaric acid

9 chavicol 18.52 C9H10O 134, 107, 91, 77

10 4–hydroxybenzaldehyde 18.66 C7H6O2 121, 93, 65, 39

11 4–hydroxybenzoic acid 20.92 C7H6O3 138, 121, 93, 65, 39

12 p–coumaric acid methyl ester 25.06 C10H10O3 178, 147, 119, 91, 65

13 p–coumaric acid 25.68 C9H8O3 164, 147, 119, 107, 91

Ferulic acid

14 p–vinylguaiacol 18.14 C9H10O2 150, 135, 107, 77

15 vanillin 19.26 C8H8O3 152, 123, 109, 81

16 trans–isoeugenol 20.36 C10H12O2 164, 149, 131, 103, 91, 77

17 ferulic acid 27.08 C10H10O4 194, 179, 133

152

Figure 5.3 Gas chromatograms of SPE extracts of (a) CaA; (b) pCoA and

(c) FeA solutions; subjected to Fenton oxidation at 2 min (pH 4.7,

25 °C). Numbers correspond to compound numbers in Table 5.1.

0.00

0.01

0.02

0.03

0.04

0.05

15 20 25 30 35

Inte

nsi

ty (

10

6)


8

0.00

0.40

0.80

1.20

1.60

2.00

15 20 25 30 35

Inte

nsi

ty (

10

6 )


9

10

1113

12

0.00

0.40

0.80

1.20

1.60

2.00

15 20 25 30 35

Inte

nsi

ty (

10

6 )


14

15

16

17

(a)

(b)

(c)

×

×

×

153

5.3.2 Proposed Degradation Pathways of Selected Hydroxycinnamic Acids

On the basis of the results obtained from LC/MS, HPIEC and GC/MS analyses

of the various products obtained from oxidation of HCAs using the Fenton process,

possible reaction pathways are tentatively proposed in this section with support from

the literature.

Hydroxyl radicals are mainly responsible for the degradation of HCAs. Free

radicals, in general, are unstable and highly reactive due to their unpaired electron.

Hence, it is desirable for an •OH radical to regain a lost electron, misplaced from the

catalytic decomposition of H2O2, to become stable. There are three typical fates of

these radicals as their main purpose is to become stable in the presence of other

molecules: (i) addition to a π-bond; (ii) atom transfer; and (iii) radical combination.

However, when a free radical reacts with another compound, it removes an electron

from that compound and in turn, that compound becomes a free radical. Hence, this

leads to a sequence of reactions until the reaction is terminated when two radicals

react with each other to give a non-radical species.

In this context, it is probable that the oxidative degradation of HCAs begins

with the electrophilic attack of the •OH radical. Therefore, it was suggested that the

position with the highest electron density is the most probable site for the HCAs to be

attacked by •OH (Marusawa et al., 2002). Figure 5.4 shows the electrostatic potential

maps and the equilibrium geometries of each of the three HCAs investigated, where

red indicates a negative charge and blue indicates a positive charge.

Observing the carbon atoms of each HCA molecule, there are various areas

coloured in orange and yellow which show a slightly higher negative charge than

those areas coloured in blue or green (i.e., positive charge). To determine the

intensity of these charges, the electron density distribution was calculated for each

atom of each HCA molecule. Table 5.4 shows the natural electron density

distribution of the carbon atoms of each HCA molecule. It is obvious from both

Figure 5.4 and Table 5.4, that the C8 atom has the highest electron density of –0.371,

–0.374 and –0.370 for CaA, pCoA and FeA, respectively. Hence, this is the most

potential site for the electrophilic attack of the •OH radical.

154

Figure 5.4 Electrostatic potential maps and equilibrium geometries of

(a) CaA; (b) pCoA and (c) FeA as derived from B3LYP/6-31+G*

calculations.

So, the initial degradation pathway is an attack at the C8 atom by the •OH

radical. The mechanism proposed by Krimmel et al. (2010), as depicted in Scheme

5.1, shows the formation of a new bond involving the •OH radical and one electron

from the π-bond (of the vinyl functional group) of the HCA (1). The other electron

from the same π-bond is transferred to the more stable carbon atom (2)

(i.e., a secondary (2°) radical). The 2° radical (2) is oxidised in air to form a peroxyl

radical (3).

(a) (b)

(c)

C1

C2

C3

C4 C5

C6

H1

O3

H2

O4

H6 H4

H3

H9

C7

C8 C9 H8

O1

O2 H10

C5 C4

C3

C2 C1

C6

C7

C8 C9

O4

O1

O2 H10

H9

H8

H1

H3

H11

H4 H6

C1

C2

C3

C4

C5

C6

C7

C8

C9

C10

O1

O2

O4

O3

H10

H9

H8

H3

H4

H1

H2

H5

H7

155

Table 5.4 Electron density distribution of carbon atoms in HCA molecules.

Natural atomic charges

Caffeic acid p–Coumaric acid Ferulic acid

C1 –0.248 –0.179 –0.230

C2 –0.112 –0.130 –0.116

C3 –0.203 –0.178 –0.204

C4 –0.302 –0.314 –0.300

C5 +0.291 +0.347 +0.286

C6 +0.277 –0.288 +0.268

C7 –0.120 –0.119 –0.120

C8 –0.371 –0.374 –0.370

C9 +0.785 +0.785 +0.785

C10 – – –0.313

R

OH

OH

OH

H

H

H

HOH

R

OH

OH

O

OH

H

H

H

H

H

O2

R

OH

OH

O

OH

H

H

H

H

H

OO

R = H (pCoumaric acid)

R = OH (Caffeic acid)

R = OCH3 (Ferulic acid)

(1) (2) (3)

6

5

43

21 7

8

9

Scheme 5.1

156

The peroxyl radicals (3) can undergo numerous fragmentation and

rearrangements reactions. However, based on the works presented by von Sonntag

and Schuchmann (1991), it is most probable that a bimolecular reaction occurs

between two equivalents of peroxyl radicals (3), as shown in Scheme 5.2. The

subsequent losses of oxygen atoms from each of the two peroxyl radicals (3) form

oxyl radicals (4). This is then followed by the molecular rearrangement and

subsequent fragmentation of the oxyl radicals (4) to produce two equivalents of an

aldehyde (5) and another 2° radical (6).

R

OH

OH

O

OH

H

H

H

H

H

OO

-O2

2

R

OH

OH

O

OH

H

H

H

H

H

O

2

2

R

OH

H

H

H

O

H

OH

O

OHH

2+

R = H (4Hydroxybenzaldehyde)

R = OH (Protocatechuic aldehyde)

R = OCH3 (Vanillin)

(5)

(3) (4)

(6)

Scheme 5.2

157

A unimolecular reaction between the newly formed 2° radical (6) and O2 gives

an α–hydroxyperoxyl radical (7) as shown in Scheme 5.3. Elimination of the

perhydroxyl radical (HO2•) then occurs by the simultaneous dissociation of the C–O

bond and the intramolecular transfer of the hydrogen atom from one oxygen atom to

another (8–9), which then gives glyoxylic acid (10) (Denisov and Denisova, 2006).

Hence, based on the presences of the phenolic aldehydes produced from the

corresponding HCAs as well as the presence of glyoxylic acid detected by HPIEC

(cf. Table 5.2), it is suggested that the formation of phenolic aldehydes from HCAs

via the Fenton process is likely to occur by the reaction pathways described in

Schemes 5.1–5.3.

OH

O

OHH

+O2

OH

O

OH

O

O

H

HO2 +

OH

O

H

OO

OH

OH

O

H

OO

OH

OH

O

H

O

Glyoxylic acid

(10)

(6) (7) (8) (9)

Scheme 5.3

To date, there has been no work reported on the direct oxidative degradation

of phenolic aldehydes in aqueous systems. However, a majority of work published in

the literature on the degradation of phenolic compounds has been on phenolic acids,

particularly hydroxybenzoic acids (Beltran-Heredia et al., 2001; Heredia et al., 2001;

Peres et al., 2004). Aldehydes can easily be oxidised in air to yield carboxylic acids

because the hydrogen atom from the –CHO functional group can be abstracted during

oxidation (Larkin, 1990). Under the operating conditions of both aqueous and sucrose

158

systems, there are numerous reactions that can take place for the conversion of

aldehydes to carboxylic acids such as oxidation with O2 (Larkin, 1990), radical

formation (McElroy and Waygood, 1991), Dakin oxidation and the Cannizzaro

reaction. However, both the Dakin oxidation and Cannizzaro reactions are optimum

under basic conditions, with the latter only applicable to aldehydes without α–

hydrogen atoms. It is still possible that these two reactions can take place under mild

acidic conditions (e.g., pH 4.0–6.0). However, as an aromatic alcohol was not

detected in the present study, the Cannizzaro reaction may not have taken place.

Moreover, the Dakin oxidation reaction may not have taken place, as no benezediols

or dihydroxybenzenes were detected in the present study.

A more possible oxidation pathway for the conversion of aldehydes to

carboxylic acids is via a combination of hydration, radical formation (i.e., hydrogen

abstraction) and oxidation reactions (McElroy and Waygood, 1991; Chudasama et al.,

2010). The first reaction is a reversible reaction that is in equilibrium between the

aldehyde and the aldehyde hydrate (Scheme 5.4). In this case, the phenolic aldehyde

(11) rapidly undergoes hydration to form an aldehyde hydrate (12).

R

OH

O

H

R = H (4Hydroxybenzaldehyde)

R = OH (Protocatechuic aldehyde)

R = OCH3 (Vanillin)

(5) (11)

HO

H

R

OH

O

HO H

H

(12)

R

OH

OH

HOH

R = H, OH, OCH3 R = H, OH, OCH3

Aldehyde Hydrate

Scheme 5.4

As shown in Scheme 5.5, the •OH radical abstracts a hydrogen atom from the

aldehyde hydrate (12) forming water and a tertiary (3°) radical (13). A peroxyl

radical (14) is formed by the oxygenation of the 3° radical. Dissociation of the C–O

bond and intramolecular transfer of the hydrogen atom between oxygen atoms

(15–16), eliminates HO2• and give a phenolic acid (i.e., hydroxybenzoic acid) (17).

159

(12)

R

OH

H

H

H

OH

HOH

R = H, OH, OCH3

Aldehyde Hydrate

OH

(13)

R

OH

H

H

H

OH

OH

R = H, OH, OCH3

+OH2

O2

(14)

R

OH

H

H

H

OH

OH

O

O

R = H, OH, OCH3

(15)

R

OH

H

H

H

OH

O

O

O

H

R = H, OH, OCH3

(16)

R

OH

H

H

H

OH

O

O

O

H

R = H, OH, OCH3

(17)

R

OH

H

H

H

OH

O

+ HO2

R = H (4Hydroxybenzoic acid)

R = OH (Protocatechuic acid)

R = OCH3 (Vanillic acid)

Scheme 5.5

The oxidative pathway for the degradation of hydroxybenzoic acids via the

Fenton process have been established previously in numerous reports (Rivas et al.,

2002; Rivas et al., 2005; Duesterberg and Waite, 2007). Some of the intermediate

products from these oxidation reactions such as phenols, quinones and hydroquinones

were not detected in the present study. However, several carboxylic acids detected in

the present study (viz., oxalic, glyoxylic, formic and acetic) were reported to be

produced from the degradation of these intermediates. Hence, it is presumed that the

progressive degradation of the HCAs is through the following degradation reaction

pathways (Scheme 5.6).

On the basis of the kinetic models developed by Duesterberg and Waite

(2007), electrophilic attack by •OH on the hydroxybenzoic acid (17), particularly for

4–hydroxybenzoic and protocatechuic acids, leads to the formation of isomeric

hydroxycyclohexadienyl radicals (18) (Scheme 5.6). The isomers (18) are then

oxygenated to form two different peroxyl radicals, an 1,3–cyclohexadienyl (19) and

an 1,4–cyclohexadienyl radical (20) (Fang et al., 1995; Krimmel et al., 2010).

Elimination of HO2• from the 1,3–cyclohexaidenyl radical (19) produces a

160

hydroxylated phenolic acid (21), meanwhile the 1,4–cyclohexadienyl radical (20) is

subjected to further oxidation which may lead to ring-opened products.

(17)

R

OH

H

H

H

OH

O

R = H (4Hydroxybenzoic acid)

R = OH (Protocatechuic acid)

(18)

R

OH

H

H

OH

O

H OHOH

R = H, OH

(19)

R

OH

H

OH

O

H OH

O

H

O

O2

O2

(20)

R

OH H

OH

O

H OH

O HO

R = H, OH

R = H, OH

-HO2

(21)

R

OH

OH

O

OH

H

H

R = H (Protocatehuic acid)

R = OH (Gallic acid)

Ring-Opened Products

End Products

(e.g., Carboxylic Acids)

Scheme 5.6

However, for vanillic acid (22), an oxidation product of FeA, the methoxyl

group of (23) undergoes oxidative demethoxylation by •OH to produce a phenoxyl

radical (24) and methanol, as shown in Scheme 5.7 (O'Neill et al., 1977). The

phenoxyl radical (24) reacts with HO2• to form protocatechuic acid (25) and O2.

Subsequently, the newly formed protocatechuic acid can be subjected to electrophilic

addition by the •OH radical and react in the same manner as described in Scheme 5.6.

161

(22)

H3CO

OH

H

H

H

OH

O

(23)

OH

H

H

OH

OOH

H3CO

(24)

O

OH

H

OH

O

H

H

+HO2

(25)

OH

OH

OH

OH

H

H

Protocatehuic acid

+ CH3OH

Vanillic acid

HO

+ O2

Scheme 5.7

Based on the reactions discussed so far, oxidation by radicals or oxygenation

will not influence any further degradation of the hydroxybenzoic acids. Moreover,

dehydration of the hydroxycyclohexadienyl radical (29), initially produced from the

electrophilic attack on the hydroxybenzoic acid, would give a phenoxyl radical

(Anderson et al., 1987). The phenoxyl radical can rapidly undergo oxidative coupling

to form oligomers; or undergo a cycle by reacting with non-reacted hydroxybenzoic

acids, quinones or Fe(II)/Fe(III) to produce hydroxylated products (e.g.,

4–hydroxybenzoic and protocatechuic acids) (Anderson et al., 1987; Lind et al., 1990;

Chen and Pignatello, 1997).

Therefore, it is only possible that the degradation of hydroxylated (or non-

hydroxylated) hydroxybenzoic acids can occur through chelating with Fe(II)/Fe(III)

as described in Scheme 5.8. The process not only regenerates or converts

Fe(II)/Fe(III) vice versa, but also oxidises these acids to give quinones through

electron-transfer reactions. The formation of quinones can readily be attacked by

•OH radicals and consequently, opens the aromatic ring forming carboxylic acids. In

this case, the quinone produced from the reactions between Fe(II)/Fe(III) and

protocatechuic acid, is subject to •OH radical attack and decomposes to give ring-

opened products such as carboxylic acids (Duesterberg and Waite, 2007).

162

(17)

R

OH

H

H

OH

OH

O

(26)



R

OH

H

H

OH

OH

O

Fe

R = H, OH

Fe(III)

(27)

R

OH

H

OH

OH

O

H

-Fe(II)

R = H, OH

(28)

R

O

H

O

OH

O

H

1. Fe(III)

R = H, OH

2. -Fe(II)

Ring-Opened Products

OH

End Products

(e.g., Carboxylic Acids)

Scheme 5.8

An alternate and more probable pathway for the opening of the aromatic ring

than the pathway proposed by Duesterberg and Waite (2007), previously shown in

Scheme 5.8 is through the initial decarboxylation of the hydroxybenzoic acid to give a

phenol. Subsequent •OH radical attack on phenol would result in ring-opening of the

aromatic ring and give a carboxylic acid.

Scheme 5.13 shows the postulated oxidation pathways of protocatechuic acid

and gallic acid leading to the formation of carboxylic acids based on the kinetic

models developed by Rivas et al., (2005) and Chen and Pignatello (1997). Hydroxyl

radical attack on the hydroxybenzoic acid (17) followed by radical abstraction by

Fe(III) gives a stable decarboxylated phenol (30). The phenol (30) can then undergo

two reaction pathways. The first pathway is with Fe(III) to form a quinone (33)

which will then lead to formation of a C6 dicarboxylic acid (34). The other pathway

is through hydrogen abstraction, followed by the elimination of the HO2• radical to

give an intermediate (32). The intermediate (32) is then subjected to subsequent

hydrogen abstraction and HO2• elimination to give a C6 dicarboxylic acid with a

hydroxyl group at the C3 position (35). Alternatively, the intermediate (32) can react

with Fe(III) to produce a phenoxyl radical intermediate (36).

163

(17)

H

OH

H

H

OH

OH

O

(29)



R

OH

H

H

OH

H

R = H, OH

+ H2O CO2+

1. Fe(III)

2. H

OH

(30)

R

OH

H

H

OH

H

R = H, OH

+Fe(II)

1. Fe(III)

2. H

R

OH

H

H

O

H

1. OH

2. O2

(32)

R

OH

H

OH

H

OH

R = H, OH

+ HO2

1. Fe(III)

2. H

R

OH

H

OH

H

OFe(II) +

(31)

R = H, OH

(36)

R = H, OH

+ Fe(II)

1. Fe(III)

2. H

R

O

H

H

O

H

(33)

R = H, OH

+ Fe(II)

1. OH

2. O2

OHOH

O

OH O

R

(35)

R = H, OH

OH

OHOH

O

R O

(34)

R = H, OH

(cf., Scheme 5.10)

Scheme 5.9

164

Further oxidation of the phenoxyl radical intermediate (36) gives two

equivalents of malonic acid or tartronic acid (38) from protocatechuic acid and gallic

acid, respectively (Scheme 5.14). Oxidation of these carboxylic acids (38) could give

rise to acetic and glycolic acid (39), which in turn decomposes to formic acid (40) and

mineralises to CO2 and H2O (Cui et al., 2012).

(38)

(37)

R

OH

H

OH

H

O

R = H, OH

+ Fe(II)

1. Fe(III)

2. H

R

OH

H

OH

H

O

(36)

R = H, OH

OH

OHOH

O O

R

2

R = H (Malonic acid)

R = OH (Tartronic acid)

OH

OH

O

R

+ H2O + CO2

(39)

R = H (Acetic acid)

R = OH (Glycolic acid)

H2O + CO2

OH

HOH

O

(40)

(Formic acid)

Scheme 5.10

165

The C6 dicarboxylic acids produced from the ring cleavage of (32) and (33)

by the •OH radical can oxidise and break down to smaller products. For example, as

shown in Scheme 5.11, the cleavage of one of the carbon-carbon unsaturated bonds of

muconic acid (34), produced from protocatechuic acid, gives rise to two intermediates

(41) and (42). In the presence of O2, •OH radical attack on the intermediate (41)

gives oxalic acid (43). Oxalic acid (43) under these conditions readily decomposes to

formic acid (40) and mineralises to CO2 and H2O (Rivas et al., 2005; Cui et al.,

2012). Moreover, the aldehyde functional group in glyoxylic acid (10), produced

from the cleavage of the vinyl functional group of the HCAs (Scheme 5.3), can

oxidise in the same manner as described in Scheme 5.4 and 5.5 to give oxalic acid

(43).

OHOH

O

O

(34)

OH

H

O

OH

OOH

O

HOH

OOH

+

(41) (42)

OH

(43)

OHOH

O

O

2

(Oxalic acid)

OH+ H2O + CO2

H2O + CO2

HOH

O

(40)

(Formic acid)

(Muconic acid)

Scheme 5.11

166

The remaining carboxylic acids detected in the HCA mixtures; cis–aconitic

acid, butyric acid, isobutyric acid and lactic acid have not been reported in any of the

proposed mechanisms published in the literature. It is probable that these acids may

not have originated from the aromatic moiety of the HCAs. It is hypothesised that the

formation of butyric, isobutyric and lactic acids may have originated from the

aliphatic substituent of the HCAs after bond cleavage of the vinyl functional group.

On the other hand, the presence of cis–aconitic acid may have also originated from

the aliphatic substituent. Additional carboxylic acid groups on cis–aconitic acid may

have originated from other reaction intermediates and products as a result of radical

combination.

5.3.3 Oligomerisation of Selected Hydroxycinnamic Acids

From the results obtained from the LC/MS analyses, oligomeric products were

formed within 2 min of oxidation via the Fenton process. Oxidative coupling

reactions can lead to the formation of dimer products consisting of two equivalents of

HCAs (Antolovich et al., 2004). Moreover, reactions with organic radicals produced

from HCAs, are susceptible to further polymerisation to form higher oligomers such

as trimers and tetramers (Arakawa et al., 2004). Initial oligomerisation of HCAs can

possibly lead to the formation of two types of products, cyclodimers and

dehydrodimers (Ford and Hartley, 1990; Dobberstein and Bunzel, 2010). However,

cyclodimerisation reactions are photochemical and not dependent on reactions

involving free radicals (i.e., radical coupling) (Ford and Hartley, 1989).

In Scheme 5.1, the electrophilic addition of the •OH radical on a HCA was

shown. However, radicals in general not only partake in addition reactions but can

also go through abstraction and radical combination reactions as well. In Scheme

5.12, hydrogen atom abstraction by the •OH radical from the phenolic functional

group can occur, giving rise to the formation of a cinnamoyl radical (44).

167

R

O

OH

O

H

OH

R

O

OH

O

R = H (pCoumaric acid)

R = OH (Caffeic acid)

R = OCH3 (Ferulic acid)

(1)

OH2 +

R = H, OH, OCH3

(44)

Scheme 5.12

Possible resonance structures of the cinnamoyl radical are shown in Scheme

5.13, where (45) and (48) are the least and most stablilised forms of the radical by

resonance, respectively. Due to electron delocalisation of this radical, the coupling of

two cinnamoyl radicals (i.e., radical combination) can theoretically give rise to the

formation of dimers through 5–5-, 4–O–5-, 8–5-, 8–8-, and 8–O–4-coupling (Bunzel,

2010).

R

O

OHO

R = H, OH, OCH3

(45)

R

O

OHO

R = H, OH, OCH3

(46)

R

O

OHO

R = H, OH, OCH3

(47)

R

O

OHO

R = H, OH, OCH3

(48)

1

6

54

2

3

7 8

9

Scheme 5.13

168

The structure of the FeA dimer was determined on the basis of the MS

fragments obtained from the LC/MS data with confirmation from literature data

(Antolovich et al., 2004). The MS spectrum for the peak at tR = 18.87 min

(cf. Appendices, Figure A2.4), tentatively assigned to the FeA dimer shows fragments

at m/z 385, 341, 297 and 155. Losses of two CO2 molecules suggest that the

formation of the FeA dimer, under the Fenton conditions investigated was due to the

5–5-coupling of feruloyl radicals (Scheme 5.14).

H+

OCH3

OH

OH

O

H3CO

OH

OH

O

CO2

H+

OCH3

OH

CH2

H3CO

OH

OH

O

CO2

OCH3

O

CH2

H3CO

O

CH2

H

H

H+

m/z 385 m/z 341

m/z 297

Scheme 5.14

169

The structure for the tetramer of CaA was not determined due to insufficient

information obtained from the MS data. The MS spectrum of the peak at

tR = 12.75 min tentatively assigned to the tetramer of CaA had fragment ions of

m/z 715, 471, 357, 269 and 145 (cf. Appendices, Figure A2.5).

Caffeic acid, [M]– has a m/z of 179 and its deprotonated dimer from any of the

five suggested radical coupling reaction pathways would give a m/z of 357 (Arakawa

et al., 2004). Hence, further deprotonation between the coupling of two equivalents

of CaA dimers or the subsequent coupling of two caffeoyl radicals in succession to

the CaA dimer would give rise to a tetramer that would have a m/z ratio of 713 (Pati et

al., 2006), not a m/z ratio of 715. Representative structures of other tetramers of CaA

that exist naturally in plants have a m/z ratio of 713 (Bunzel, 2010). So, it is probable

that the m/z 715 ion is an adduct of the CaA dimer.

The fragmentation of m/z 357 proposed by Pati et al. (2006) is given in

Scheme 5.15. Loss of two CO2 molecules from the 5–5-coupled dimer (m/z 357)

gives a structure for the m/z 269 ion. Ring opening of one of the aromatic moieties of

the dimer and subsequent rearrangement reactions lead to the formation of a quinone

fragment ion (m/z 159) and a neutral fragment (M = 110). The m/z ion of 393 is

possibly attributable to the CaA dimer and two H2O molecules [M–2H2O]–.

The m/z ions 471 and 715 could not be elucidated on the basis of the current

information ascertained. Agha et al. (2009) proposed a structure for a tetramer of

CaA with a m/z ratio of 715, showing the coupling of a 8–8-coupled dimer and a 5–5-

coupled dimer, both at the O–4 positions (Figure 5.5). However, it is not possible for

the oxygen atoms to be coupled under these circumstances due to the instability of the

O–O single bond. In addition, it is highly unlikely for a phenolic group in only one of

the CaA moieties to exist in a semiquinone form within the tetramer. From the

foregoing, the structure of the presumed CaA tetramer (or CaA dimer adduct)

assigned to the m/z ion of 715 is not known.

170

OH

O

CH2

O

O

H

CH2

O

O

CH2

O

O

CH2

H

O

O

CH2

CH2

O

O

CH2

+

H+

OH

OH

OH

O

OH

OH

OH

O

2CO2

OH

O

CH2

H

OH

O

CH2

H

H+

m/z 357 m/z 269

H+

m/z 269

H+

m/z 269

H+

m/z 159 M = 110

Scheme 5.15

171

OH

O

OH

O

O

OHOH

O

OH

O

OH

O

OH

OH

OH

O

H+

Figure 5.5 Proposed structure of a tetramer of caffeic acid (m/z 715) by Agha

et al., (2009).

5.4 Summary

This chapter outlines an intense treatise in organic chemistry in an attempt to

determine whether the degradation products of three selected phenolic acids when

subjected to Fenton oxidation were liable to be colour precursors. Attempts were

made to identify and propose the tentative reaction pathways of oxidation products of

CaA, pCoA and FeA via the Fenton process. Eleven aromatic products and eight

aliphatic products were identified. Cleavage of the conjugated vinyl substituent of the

HCAs by electrophilic addition of •OH; and hydrogen abstraction from the phenol

group of the HCAs were the two major mechanisms initiating the degradation of HCA

via the Fenton oxidation process. The initial products undergo a series of successive

oxidation steps which lead to the formation of carboxylic acids.

172

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176

177

CHAPTER 6

Degradation of Melanoidin and

Hydroxycinnamic Acid Mixtures

6.1 Introduction..................................................................................... 177



6.2.2 Preparation of Synthetic Melanoidin.................................. 179

6.2.3 Modified Fenton Oxidation Process.................................... 179


6.2.5 Performance Assessment of the Modified Fenton

Oxidation Process...............................................................

180

6.2.6 Design of Experiments......................................................... 181

6.2.7 Statistical Analysis.............................................................. 182


6.3.1 Monitoring Melanoidin and Hydroxycinnamic Acid

Degradation.........................................................................

182

6.3.2 Transformation of Data, Regression Modelling and

Statistical Analysis..............................................................

184

6.3.3 Oxidation Performance of Melanoidins.............................. 190

6.3.4 Oxidation Performance of Hydroxycinnamic Acids............ 194

6.3.5 Response Surface Analyses for the Decolourisation of

Mixtures...............................................................................

198

6.3.6 Process Optimisation and Validation.................................. 200

6.4 Summary.......................................................................................... 203

178

6.1 Introduction

The Fenton process has been shown to oxidise and degrade model colour

precursor compounds in water and sucrose solutions. However, the numerous

oxidation products formed and the presence of starting materials (viz., CaA, pCoA

and FeA) indicate that the Fenton process on its own, would not effectively

decolourise factory sugar cane juice.

Dwyer et al., (2009) studied the removal of factory and synthetic melanoidins

(factory produced colourants during sugar manufacture), using hydrated aluminium

sulfate (Al2(SO4)3.xH2O). The results from the study showed that an Al2(SO4)3 dose

of 30 mg/L as aluminium ion (Al(III)) was sufficient to remove 75% of colour from

factory effluents contaminated with melanoidins. Aluminium is known for its

significant pro-oxidant activity, and so can enhance the Fenton process by reducing

Fe(III) to Fe(II) under the presence of superoxide (Exley, 2004; Ruipérez et al.,

2012). This is because Fe(II) is a more effective catalyst for H2O2 decomposition

than Fe(III).

This study builds on these investigations by examining the degradation and

decolourisation of a complex mixture of a synthetic melanoidin and HCAs using a

modified Fenton process consisting of Fe(II), Al(III) and H2O2.



All chemicals, solvents and reagents were of AR grade and obtained from the

suppliers as described in the previous chapters or as otherwise stated. Aluminium

chloride hexahydrate (AlCl3·6H2O) and glycine were supplied from Merck

(Darmstadt, Germany). Stock solutions of hydroxycinnamic acids, HCAs (i.e., CaA,

pCoA and FeA) were prepared individually by dissolution in degassed ethanol

solution (50% (v/v)) and stored at 4.0 °C.

179

6.2.2 Preparation of Synthetic Melanoidin

The synthesis procedure used for the preparation of a synthetic melanoidin

was adapted from Shore et al., (1984). Glucose (72 g) and glycine (30 g) were

dissolved in water (60 mL) and incubated at 50 °C for 72 h. The resulting co-polymer

was then stored at 4.0 °C.

6.2.3 Modified Fenton Oxidation Process

The procedure for the degradation of the mixtures is similar to that described

in Sections 4.2.4 and 5.2.2. In each run, a predetermined amount of Milli-Q water,

melanoidin, sucrose and each HCA (equivalent mg/L concentration) were added to

the reaction vessel. Known amounts of FeSO4·7H2O, AlCl3·6H2O and H2O2 solutions

were added to achieve a final volume of 50 mL, while maintaining the working

Fenton molar ratio (Fe(II)/H2O2) at 1:15. The final sucrose concentration in each

mixture was 15% (w/w). At 2 min, the reaction was immediately quenched by

neutralising the mixture to a pH of 7.0 with 2.0 M NaOH and stored at –80 °C.

Samples were defrosted and prepared for instrumental analysis.


HPLC-DAD/FLD. The proportion of each HCA and melanoidin degraded

were monitored by reversed-phase HPLC-DAD and fluorescence detection (FLD).

The analysis was performed on a Hewlett Packard HP/Agilent 1100 Series HPLC

system (G1379A micro-degasser, Japan; G1311A quaternary pump, Germany;

G1313A Automatic Liquid Sampler (ALS), Germany; G1315B DAD, Germany;

G1321A FLD, Germany) using a Waters Symmetry C18 column (150 × 3.9 mm i.d.)

with a Waters Guard-Pak guard holder containing a Waters Guard-Pak Resolve C18

guard insert (10 μm) (Milford, MA, USA). The mobile phase consisted of 1.0% (v/v)

acetic acid in water (as eluent A) and methanol (as eluent B). The gradient program

was as follows: 0% B to 10% B (1 min), 10% B to 20% B (1 min), 20% B to 25% B

(3 min), 25% B to 50% B (15 min) and 50% B to 20% B (5 min). Simultaneous

UV/Vis detection at specific wavelengths (280 nm and 320 nm) subtracted against a

180

reference wavelength (620 nm). Fluorescence detection was performed at an

excitation wavelength (λex) of 350 nm and an emission wavelength (λem) of 445 nm.

Aliquots of samples were membrane filtered (0.45 μm) prior to injection into the

HPLC system. Injection volume for all samples was 50 μL; column temperature was

ambient; flow rate was 1.0 mL/min and run time was 25 min. After each run, the

chromatographic system was equilibrated for 5 min. Data acquisition was performed

using the Agilent ChemStation (Rev. A.09.03) software package. Identification of

peaks was based on the conformance of UV/Vis spectra and retention times with the

corresponding authentic standards.

HPAEC-PAD. Sucrose and reducing contents in the reactions were monitored

by HPAEC-PAD. Sample preparation and chromatographic conditions are described

in Section 4.2.5.

6.2.5 Performance Assessment of the Modified Fenton Oxidation Process

The efficiency of the modified Fenton process on the degradation of the CaA,

pCoA and FeA was determined individually based on the change in absorbance of the

corresponding HPLC chromatographic peak using Equation 4.1. The degradation

efficiency of the melanoidin was determined on the basis of the changes in

luminescence of the corresponding HPLC chromatographic peak using Equation 6.1:

0

0

% Melanoidin degradation = 100tL L

L

(6.1)

where, L0 initial luminescence of melanoidin in LU (at t = 0 min)

Lt luminescence of melanoidin in LU at time of aliquot taken

(at t = 2 min)

The decolourisation efficiency of the synthetic mixtures was determined based

on the change in colour of the mixtures prior to oxidation and at 2 min, using

Equation 6.2. Procedures for the measurements of colour, RI and TSS are described

in Section 3.2.6.

181

0

0

% Decolourisation = 100tColour Colour

Colour

(6.2)

where, Colour0 initial colour of the mixture in IU (at t = 0 min)

Colourt colour of the mixture in IU at time of aliquot taken

(at t = 2 min)

6.2.6 Design of Experiments

Design of experiments, mathematical modelling and optimisation of process

parameters were evaluated using the Stat-Ease, Inc. Design-Expert 7.0.0 software

package (Minneapolis, MN, USA).

A rotatable circumscribed CCD with a half-fractional factorial was used to

evaluate the main effect for each condition and the possible interactive effects on the

residual stresses between two variables. The process parameters (independent

variables) used in this study were the melanoidin concentration (x1), the initial total

HCA concentration (x2), the solution pH (x3), FeSO4·7H2O dosage (x4) and the

AlCl3·6H2O (x5). The selected response factors (dependent variable) for optimisation

were % melanoidin degradation (y1), % total HCA degradation (y2) and

% decolourisation (y3). Sucrose concentration and temperature parameters required

no further optimisation and remained constant at 15% (w/w) and 35 °C respectively,

to closely mimic conditions of MJ during the sugar manufacturing process. The

coded and actual values of each variable and their levels for the experimental design

used in the study are shown in Table 6.1.

182

Table 6.1 Coded and actual values of the experimental design.

Coded Levels of Parameters

Notation Factor Unit –2 –1 0 +1 +2

A (x1) Melanoidin mg/L 0 500 1000 1500 2000

B (x2) Total HCA mg/L 0 50 100 150 200

C (x3) pH 4.50 4.88 5.25 5.63 6.00

D (x4) FeSO4·7H2O mg/L 86.0 238 389 541 692

E (x5) AlCl3·6H2O mg/L 0 100 200 300 400


centre point, where k is the number of factors investigated (k = 5). For this study,

when the one-half fraction is used in the factorial portion of the CCD, a total of 32

experiments were conducted in random order with 16 factorial points, 10 axial points

and 1 centre point (duplicated 5 times). Duplicate runs were required for

experimental error calculation.

6.2.7 Statistical Analysis

Analysis of variance was used for model adequacy and analysis of the

experimental data. The quality of the fit polynomial model was expressed by the

regression coefficient, R2 and its statistical significance was checked using Fisher’s

F-test. Model terms were determined based on the significance of each term at a

confidence level of 95%.


6.3.1 Monitoring Melanoidin and Hydroxycinnamic Acid Degradation

The reversed-phase HPLC analyses show that the melanoidin components

were eluted between tR = 0.85–5.0 min (Figure 6.1), while the HCAs were eluted

between tR = 8.5–13.5 min (Figure 6.2). The FLD method is a far more superior

detection method than the DAD method for the monitoring of melanoidins and humic-

183

like substance, as evident from the chromatogram of Figures 6.1 and 6.2 (Westerhoff

et al., 2001). Meanwhile, the latter is more sensitive for the detection of HCAs.

Figure 6.1 shows that the synthetic glucose-glycine melanoidin consists of

several products which are relatively polar in comparison to the non-polar HCAs. A

distinctive large peak prior to oxidation (t = 0 min) at tR = 2.11 min was chosen to

monitor the degradation of the melanoidin. At 2 min, the peaks at tR = 1.79 and 2.11

min at tR = 1.79 min are reduced in size. However, the peak at tR = 1.18 min

increased in size. This probably suggests that the components associated with tR =

1.79 and 2.11 min are oxidised to polar compounds at 2 min. On the other hand, a

large response was observed for the peak at tR = 1.18 min after oxidation. This

suggests that the two components of the melanoidin present at t = 0 are being oxidised

to form polar compounds.

Figure 6.1 Typical HPLC-FLD chromatogram (fluorescence detection at

λex = 350 nm and λem = 445 nm) of the melanoidin/HCA mixture in

sucrose solution (15% (w/w)) before and after modified Fenton

oxidation (t = 2 min) at pH 5.6 and 35 °C.

0 2 4 6 8 10

Ab

sorb

an

ce (

Arb

itra

ry U

nit

s)


t = 0 min

t = 2 min

184

Figure 6.2 Typical HPLC-DAD chromatogram (UV/Vis detection at 280 nm)

of the melanoidin/phenolic acid mixture in sucrose solution

(15% (w/w)) before and after modified Fenton oxidation

(t = 2 min) at pH 5.6 and 35 °C. 1 = CaA, 2 = pCoA, 3 = FeA.

6.3.2 Transformation of Data, Regression Modelling and Statistical Analysis

Non-linearity of normal probability plots of residuals for the fitted models of

melanoidin and total HCA degradation were resolved via Box-Cox power

transformation (cf. Appendices, Figure A3.1). The optimum λ values determined by

the minimum of the curve of the Box-Cox plots for the degradation of the melanoidin

and total HCA were –3.00 and –0.35 respectively (cf. Appendices, Figure A3.2). The

fitted model for decolourisation did not require any data transformation (λ = 1.00) as

the internally studentised residual points resembled a linear curve. The data for all

fitted response surface models show good correspondence to a normal distribution

and validated the normality assumption.

On the basis of the sequential model sum of squares (Type I), the power

transformed response surface models for melanoidin degradation (y1) and total HCA

degradation (y2); as well as the response surface model for decolourisation (y3) were

selected based on the highest order polynomial, where the additional model terms

were significant and the models were not aliased. The degradation data obtained for

the melanoidin and total HCA responses fit a two-factor interaction (2FI) function,

4 6 8 10 12 14

Ab

sorb

an

ce (

Arb

itra

ry U

nit

s)


t = 0 min

t = 2 min

1

2

3

185

while the data for the decolourisation of the mixtures fits a quadratic polynomial

function.

Selection of significant coefficients and removal of unimportant model terms

for each model were identified on the basis of ANOVA statistics and stepwise

regression at an alpha-to enter and alpha-to-exit significance level of 0.1. The chosen

stepwise alpha range applied to all three response surface models should result in final

models with significant model terms included at the approximate 95% confidence

level.

The ANOVA results for the partial sum of squares (Type III) for the three

response surface reduced quadratic or 2FI models after stepwise regression are shown

in Tables 6.2–6.4. The analyses indicate that most of the independent variables and

some of the interactions are significant and contribute to the degradation of the

melanoidin and the HCAs, as well as the decolourisation of the mixtures. The model

F-values of 8.09, 8.96 and 18.27 for melanoidin degradation, total HCA degradation

and decolourisation respectively, imply that the models are significant. There is only

a 0.01% chance that a model F-value this large could occur due to noise. The lack of

fit F-values of 0.47, 0.72 and 1.91 for the melanoidin degradation, total HCA

degradation and decolourisation models in that order imply that the lack of fit is not

significant relative to pure error. There is a 87.52%, 70.95% and 27.86% chance

respectively that the lack-of-fit F-values this large would occur due to noise. Non-

significant lack of fit is good as it confirms the predictability of the model.

The independent variables in the models were initial melanoidin

concentration, initial total HCA concentration, solution pH, FeSO4·7H2O dosage and

AlCl3·6H2O dosage; and were coded A, B, C, D and E respectively. The final

empirical quadratic equations in terms of coded factors for each response after the

exclusion of the insignificant model terms (p > 0.1000), unless retained to make the

models hierarchical, are as follows:

186

Melanoidin degradation (%)

(y1)–3 = 3.447 × 10–6 – 1.614 × 10–7A + 1.286 × 10–7B

+1.764 × 10–7C – 8.041 × 10–8D – 1.853 × 10–8E

+ 1.938 × 10–7AE + 2.606 × 10–7CD + 1.362 × 10–7CE

– 4.267 × 10–7DE

(6.3)

Total HCA degradation (%)

(y2)–0.35 = 0.26 + 1.094 × 10–3A – 1.207 × 10–4B + 1.219 × 10–3C

– 2.202 × 10–3D + 5.081 × 10–4E + 1.737 × 10–3AC

– 4.064 × 10–3AD + 1.926 × 10–3BD – 2.491 × 10–3BE

(6.4)

Decolourisation (%)

(y3) = 13.78 + 12.06A – 1.49B + 7.11C – 9.66D – 3.34E

+ 14.83AE + 23.60BC – 16.89BD – 12.99BE + 9.12CD

– 13.28DE – 10.78A2 + 3.89E2

(6.5)

The predicted R2 values of all response surface models are in reasonable

agreement with the adjusted R2 values, which show that the fitted models are

adequate. Plots of the predicted response against the experimental values for the three

models show good linearity, indicating that the developed mathematical models are

suitable for predicting melanoidin degradation, total HCA degradation and

decolourisation (cf. Appendices, Figures A3.2–A3.3).

187


reduced two-factor interaction model for melanoidin

degradation.*


Model 5.22 × 10–12 9 5.80 × 10–12 8.09 < 0.0001 Significant

A 4.61 × 10–13 1 4.61 × 10–13 6.43 0.0207 Significant

B 3.65 × 10–13 1 3.65 × 10–13 5.09 0.0368 Significant

C 5.68 × 10–13 1 5.68 × 10–13 7.92 0.0115 Significant

D 1.15 × 10–13 1 1.15 × 10–13 1.60 0.2224 Insignificant

E 7.58 × 10–15 1 7.58 × 10–15 0.11 0.7488 Insignificant

AE 5.31 × 10–13 1 5.31 × 10–13 7.41 0.0140 Significant

CD 9.60 × 10–13 1 9.60 × 10–13 13.39 0.0018 Significant

CE 2.62 × 10–13 1 2.62 × 10–13 3.66 0.0719

DE 2.57 × 10–12 1 2.57 × 10–12 35.90 < 0.0001 Significant

Residual 1.29 × 10–12 18 7.17 × 10–12

Lack of Fit 7.08 × 10–13 13 5.44 × 10–13 0.47 0.8752 Insignificant

Pure Error 5.83 × 10–13 5 1.17 × 10–13

Corr. Total 6.51 × 10–12 27

Criteria

Standard Deviation 2.68 × 10–07

Mean 3.41 × 10–06

CV (%) 7.85

PRESS 3.14 × 10–12

R2 0.80

Adjusted R2 0.70

Predicted R2 0.52



Corr. (Corrected)

188


reduced two-factor interaction model for total HCA degradation.*


Model 6.20 × 10–04 9 6.88 × 10–05 8.96 < 0.0001 Significant

A 2.27 × 10–05 1 2.27 × 10–05 2.95 0.1038 Insignificant

B 2.19 × 10–07 1 2.19 × 10–07 0.028 0.8679 Insignificant

C 3.16 × 10–05 1 3.16 × 10–05 4.11 0.0585 Insignificant

D 8.18 × 10–05 1 8.18 × 10–05 10.64 0.0046 Significant

E 5.49 × 10–06 1 5.49 × 10–06 0.71 0.4098 Insignificant

AC 3.46 × 10–05 1 3.46 × 10–05 4.50 0.0489 Significant

AD 1.89 × 10–04 1 1.89 × 10–04 24.63 0.0001 Significant

BD 4.25 × 10–05 1 4.25 × 10–05 5.53 0.0310 Significant

BE 7.11 × 10–05 1 7.11 × 10–05 9.25 0.0074 Significant

Residual 1.31 × 10–04 17 7.69 × 10–06

Lack of Fit 9.15 × 10–05 13 7.04 × 10–06 0.72 0.7095 Insignificant

Pure Error 3.92 × 10–05 4 9.80 × 10–06

Corr. Total 7.50 × 10–04 26

Criteria

Standard Deviation 2.77 × 10–03

Mean 0.26

CV (%) 1.08

PRESS 3.10 × 10–04

R2 0.83

Adjusted R2 0.73

Predicted R2 0.59



Corr. (Corrected)

189


reduced quadratic model for decolourisation.*


Model 26,633.27 13 2,048.71 18.27 < 0.0001 Significant

A 1,691.99 1 1,691.99 15.09 0.0019 Significant

B 42.23 1 42.23 0.38 0.5500 Insignificant

C 762.90 1 762.90 6.80 0.0217 Significant

D 1,792.63 1 1,792.63 15.99 0.0015 Significant

E 214.13 1 214.13 1.91 0.1903 Insignificant

AE 2,557.93 1 2,557.93 22.81 0.0004 Significant

BC 6,452.02 1 6,452.02 57.54 < 0.0001 Significant

BD 3,321.20 1 3,321.20 29.62 0.0001 Significant

BE 1,962.16 1 1,962.16 17.50 0.0011 Significant

CD 967.59 1 967.59 8.63 0.0115 Significant

DE 2,043.80 1 2,043.80 18.23 0.0009 Significant

A2 1,536.86 1 1,536.86 13.71 0.0027 Significant

E2 419.92 1 419.92 3.74 0.0750

Residual 1,457.76 13 112.14

Lack of Fit 1,182.55 9 131.39 1.91 0.2786 Insignificant

Pure Error 275.20 4 68.80

Corr. Total 28,091.03 26

Criteria

Standard Deviation 10.59

Mean 14.17

CV (%) 74.74

PRESS 6,824.48

R2 0.95

Adjusted R2 0.90

Predicted R2 0.76



Corr. (Corrected)

190

6.3.3 Oxidation Performance of Melanoidins

On the basis of the coefficients of the first-order model terms in the cubed

inverse model for melanoidin degradation (Equation 6.3), it is obvious that the

degradation efficiency of the melanoidin increases with increasing initial melanoidin

concentration (A). However, it is evident that the degradation efficiency decreases

with increasing initial HCA concentration (B) and at higher initial pH (C). There

were no significant positive effects on the degradation of melanoidins at varying

dosages of FeSO4·7H2O (D) and AlCl3·6H2O (E). This suggests that the ranges of the

working dosages for the Fenton and Al(III) additives used in this study require no

further optimisation and can degrade melanoidins up to 2,000 mg/L in a sugar

solution containing 15% (w/w) sucrose. Hence, it is possible to use lower dosages of

both Fenton and Al(III) additives to effectively degrade melanoidins. However, based

on the perturbation plot for melanoidin degradation (Figure 6.3), the Fenton reagents

are more effective than Al(III) in the degradation of melanoidins. The plot confirms

that the presence of HCAs has a large influence on the degradation of melanoidins as

the •OH radicals produced during the modified Fenton process have a stronger

preference to oxidise HCAs than melanoidins.

The significant two-factor interactive parameters for the degradation of

melanoidins in the mixture via the modified Fenton oxidation process are melanoidin

concentration and AlCl3·6H2O dosage (AE); pH and FeSO4·7H2O dosage (CD); pH

and AlCl3·6H2O dosage (CE); and FeSO4·7H2O dosage and AlCl3·6H2O dosage

(DE). Contour plots (Figures 6.4 and 6.5) were used to investigate the relationships

between the pairs of interactive parameters of the developed model (Equation 6.3).

Amongst the pairs, CD (p = 0.0018) and DE (p < 0.0001) were the most statistically

significant interactions followed by AE (p = 0.0140) then CE (p = 0.0719). Hence,

the corresponding plots for AE and CE shown in Figures 6.4a and 6.5a only show

slight changes in colour associated to the amount of melanoidin degraded (64–67%).

This indicates that Al(III) contributes to the degradation of the melanoidin, only to a

small extent, with changing initial melanoidin concentration or solution pH.

Therefore, it is not necessary to use higher dosages of Al(III) with the Fenton process

to degrade melanoidins. However, there are stronger relationships between CD

(Figure 6.4b) and DE (Figure 6.5b), both showing wider ranges in the amounts of

melanoidin degraded with 65–69% and 63–69% degradation respectively.

191

Figure 6.3 Perturbation plot for (%) melanoidin degradation. Coded values

are shown for each factor: melanoidin (A); total HCA (B); pH (C);

FeSO4·7H2O dose (D) and AlCl3·6H2O dose (E); and refer to


Optimal degradation of melanoidin is achieved at increasing FeSO4·7H2O

dosage at lower initial pH (Figure 6.4b). The optimal degradation performance can be

maintained at 69% but gradually decreases with decreasing FeSO4·7H2O dosage and

increasing pH. The oxidative performance is reduced at increasing pH and

FeSO4·7H2O dosage, as it is expected that the deactivation of Fe(II) would occur by

the precipitation of Fe(III) oxyhydroxides in solution (Cortez et al., 2011).

192


(a) melanoidin and AlCl3·6H2O dosage; (b) pH and FeSO4·7H2O

dosage. Variables: melanoidin (1,500 mg/L); total HCA

(150 mg/L); pH (5.25); FeSO4·7H2O dosage (389 mg/L) and

AlCl3·6H2O dosage (200 mg/L).

(a)

(b)

193


(a) pH and AlCl3·6H2O dosage; (b) FeSO4·7H2O dosage and


HCA (150 mg/L); pH (5.25); FeSO4·7H2O dosage (389 mg/L) and


(a)

(b)

194

Interestingly, the modified Fenton oxidation performance depends on both the

dosages of both FeSO4·7H2O and AlCl3·6H2O. As shown in Figure 6.5b, lower or

higher amounts of both reagents used together would result in improved melanoidin

degradation (68–69%). However, the degradation efficiency is reduced slightly to

66%, if Fe(II) and Al(III) were dosed at any other given concentration than the

extremes (e.g., the median values of both dosages, 389 mg/L FeSO4·7H2O and

200 mg/L AlCl3·6H2O). High dosages of FeSO4·7H2O/AlCl3·6H2O and low dosages

of the other vice versa would result in poor oxidation performance on melanoidin

degradation.

6.3.4 Oxidation Performance of Hydroxycinnamic Acids

Similar to Equation 6.3, the model for the degradation of HCAs in a synthetic

mixture containing sucrose and melanoidin via the modified Fenton process is also a

negative exponent function of the independent variables (i.e., xn, where n = 1–5). On

the basis of the coefficients from Equation 6.4, FeSO4·7H2O dosage (D) is the most

influential parameter for the degradation of HCAs, where increasing the dosage of

FeSO4·7H2O for the Fenton oxidation process enhances the degradation of HCAs

within the mixture. This is also noticeable in the perturbation plot shown in Figure

6.6. However, increasing AlCl3·6H2O (E) does not assist in the degradation of HCAs

unlike for the melanoidin component within the same mixture. Hence, it can be

concluded that the removal of HCAs (or other similar phenolic compounds) is

primarily attributable to the Fenton process only.

There were no changes in total HCA degradation at any given HCA

degradation (B) tested. This shows us that the under the various modified Fenton

conditions, a consistent amount of HCA will be degraded (ca. 48%), hence, lower

dosages of the Fe(II) and Al(III) can be reduced in order to minimise costs. A similar

trend in the degradation behaviour of the HCAs to melanoidin degradation for initial

solution pH (C) was also observed. Increasing pH would result in deactivation of the

radicals and ions required to regenerate and maintain the oxidation process.

195

Figure 6.6 Perturbation plot for (%) total HCA degradation. Coded values

are shown for each factor: melanoidin (A); total HCA (B); pH (C);

FeSO4·7H2O dose (D) and AlCl3·6H2O dose (E); and refer to


Figure 6.7 and 6.8 show the contour plots for the statistically significant two-

factor interactions of the developed model for total HCA degradation (Equation 6.4)

via the modified Fenton process: melanoidin concentration and solution pH (AC);

melanoidin concentration and FeSO4·7H2O dosage (AD); total HCA concentration

and FeSO4·7H2O dosage (BD); and total HCA concentration and AlCl3·6H2O dosage

(BE). Increasing the initial pH and melanoidin concentration resulted in lower HCA

degradation (46%). However, the amount degraded increases up to 48% when both

the melanoidin concentration and initial pH decrease (Figure 6.7a). Decreasing the

melanoidin concentration and increasing FeSO4·7H2O dosage vice versa reduces the

extent of the modified Fenton process on the degradation of HCAs in solution (Figure

6.7b). However, more HCA is degraded (50%) when lower FeSO4·7H2O dosages are

used with lower concentrations of melanoidins.

196


(a) melanoidin and pH; (b) melanoidin and FeSO4·7H2O dosage.

Variables: melanoidin (1,500 mg/L); total HCA (150 mg/L); pH

(5.25); FeSO4·7H2O dosage (389 mg/L) and AlCl3·6H2O dosage

(200 mg/L).

(a)

(b)

197


(a) total HCA and FeSO4·7H2O dosage; (b) total HCA and


HCA (150 mg/L); pH (5.25); FeSO4·7H2O dosage (389 mg/L) and


(a)

(b)

198

A strong relationship between the total HCA concentration and FeSO4·7H2O

dosage is shown in Figure 6.8a. Increasing FeSO4·7H2O dosage with lower amounts

of HCAs would result in more degradation because of less uptake of OH radical by

the HCAs. However, unlike Fe(II), adding more Al(III) does not provide any benefit

in the degradation of HCAs. At an initial HCA concentration of 50 mg/L, an

AlCl3·6H2O dosage of 100 mg/L is enough to degrade nearly half of the HCAs

initially present in solution. The degradation extent of HCAs reduces with increasing

initial HCA concentration at 50 mg/L AlCl3·6H2O. However, increasing AlCl3·6H2O

dosage with increasing initial HCA concentration would maintain optimal degradation

of HCA.

6.3.5 Response Surface Analyses for Decolourisation of Mixtures

Graphical representations of the regression model (Equation 6.5) in the form

of 3D surface plots are shown in Figure 6.9. The interactions are significant as the

curvature of the surfaces is obvious.

As shown in Figure 6.9a, the initial melanoidin and Al(III) dosage

concentrations were varied, whilst the other variables, namely pH and temperature

were kept constant. At 100 mg/L of AlCl3·6H2O, the decolourisation of the mixture

reduces at initial melanoidin concentrations of ≥ 1000 mg/L. As shown in Figure

6.9a, additional colouring is obtained at higher dosages of AlCl3·6H2O with an initial

melanoidin concentration of 500 mg/L. This may indicate that residual Al(III) may be

forming complexes with other components in the system (Cornard et al., 2006;

Lapouge and Cornard, 2007). However, with a 300 mg/L dosage of AlCl3·6H2O, the

decolourisation efficiency increases smoothly with an increase in melanoidin

concentration, suggesting that Al(III) is being consumed and contributing to the

removal of melanoidins in the mixture. However, excess dosages of AlCl3·6H2O can

also give the reverse effect where colour is added into the system (e.g., 300 mg/L

AlCl3·6H2O and 500 mg/L melanoidin) (Figure 6.9a).

199

Figure 6.9 Three-dimensional surface plots of decolourisation (%) as a

function of (a) melanoidin and AlCl3·6H2O dosage; (b) total HCA

and FeSO4·7H2O; (c) total HCA and AlCl3·6H2O dosage; and (d)

FeSO4·7H2O and AlCl3·6H2O. Variables: melanoidin

(1,500 mg/L); total HCA (150 mg/L); pH (5.25); FeSO4·7H2O

dosage (389 mg/L) and AlCl3·6H2O dosage (200 mg/L).

Excess Fe(II)/Fe(III) also affects the decolourisation performance of the

modified Fenton process despite degradation of the melanoidin and the HCAs.

Increasing Fe(II) dosage may improve the oxidative degradation of the colour

precursor and colourants in the system. However, excess Fe(II)/Fe(III) can react with

non-reacted HCAs forming coloured complexes (Figure 6.9b). For optimal

decolourisation performance, lower dosages of Fe(II) should be used even if the

degradation efficiencies of the melanoidins and HCAs are reduced. However, as

shown in Figure 6.9c, the effects of Al(III) on the modified Fenton process with initial


% Decolourisation51.049

-120.544

X1 = A: MelX2 = E: AlCl3.6H2O

Actual FactorsB: Total PA = 100.00C: Initial pH = 5.25D: FeSO4.7H2O = 389.00

500

750

1000

1250

1500

100

150

200

250

300

-24

-10

4

17

31

%

Decolo

urisatio

n

A: Mel E: AlCl3.6H2O



-120.544

X1 = B: Total PAX2 = D: FeSO4.7H2O

Actual FactorsA: Glc/Gly Melanoidin = 1000C: Initial pH = 5.25E: AlCl3.6H2O = 200

50

75

100

125

150

238

313

389

465

541

-15

-2

12

26

39

%

Decolo

urisatio

n

B: Total PA D: FeSO4.7H2O Design-Expert® Software


-120.544

X1 = B: Total PAX2 = E: AlCl3.6H2O

Actual FactorsA: Glc/Gly Melanoidin = 1000C: Initial pH = 5.25D: FeSO4.7H2O = 389

50

75

100

125

150

100

150

200

250

300

-1

8

16

25

33

%

Decolo

urisatio

n

B: Total PA E: AlCl3.6H2O



-120.544

X1 = D: FeSO4.7H2OX2 = E: AlCl3.6H2O

Actual FactorsA: Glc/Gly Melanoidin = 1000B: Total PA = 100C: Initial pH = 5.25

238

313

389

465

541

100

150

200

250

300

-9

3

15

26

38

%

Decolo

urisatio

n

D: FeSO4.7H2O E: AlCl3.6H2O

(a)

(c)

(b)

(d)

E: AlCl3·6H2O

E: AlCl3·6H2O E: AlCl3·6H2O D: FeSO4·7H2O B: Total HCA

A: Melanoidin D: FeSO4·7H2O

B: Total HCA

200

HCA concentration indicate that Al(III) decolourises the system. Increasing dosages

of Al(III) significantly improves the decolourisation extent up to 45% at an initial

concentration of 50 mg/L total HCA and up to ca. 15% at an initial HCA

concentration of 150 mg/L. This suggests that there is a combined effect between

Fe(II)/H2O2 and Al(III). The Fenton process alone rapidly oxidises and degrades the

components within the system while the Al(III) acts as an adsorbent/decolourising

agent by removing the coloured products within the system (Dwyer et al., 2009).

To investigate the combined effects of Fe(II) and Al(III), the two key variables

were compared against each other as depicted in Figure 6.9d. It is evident that for

effective decolourisation of the melanoidin/HCA system, lower Fe(II) dosages and

higher Al(III) should be used.

6.3.6 Process Optimisation and Validation

Numerical optimisation was performed on the basis of the desirability function

to determine the optimum process parameters for the models developed for

melanoidin degradation, HCA degradation and decolourisation. Multi-response

optimisation was only used for the responses of melanoidin and HCA degradation

because both models are of the same function, 2FI. Meanwhile, the quadratic model

for decolourisation was optimised and validated separately.

In order to confirm the accuracy and robustness of the predicted models and

assess its reliability to predict the degradation of the melanoidin and the HCAs as well

as decolourisation, additional experiments were carried out under those conditions.

For this study, the desirability functions for the two degradation models were

combined into one value (Table 6.5). The experimental values of the additional

experiments agree well with the predicted values (in parentheses) deduced from each

of the four models. The low error in the experimental and predicted values indicates

good agreement of the results.

201


degradation of melanoidin (2,000 mg/L) and total HCA (200 mg/L)

in sucrose solution (15% (w/w)) at 35 °C; and model verification.*

Experiments

Optimum Fenton Only Worst Case

pH 5.1 5.1 6.0

FeSO4·7H2O (mg/L) 626 626 404

AlCl3·6H2O (mg/L) 265 0 151

Melanoidin degradation (%) 69 (71) 63 (65) 62 (66)

Total HCA degradation (%) 53 (56) 47 (49) 40 (42)

Desirability 0.890 0.425 0.765



RSD was < 5.0%.

It is worth mentioning that the dosages of the Fenton reagents and Al(III) are

dependent on the initial pH (Table 6.5). Under the optimum conditions, higher

dosages of FeSO4·7H2O and AlCl3·6H2O are required in order to degrade the

melanoidin and HCAs. However, there were little differences between the optimum

and worst case experiments in terms of melanoidin degradation with only an extra

7.0% degradation achieved when dosing an additional 222 mg/L FeSO4·7H2O and

114 mg/L AlCl3·6H2O to the system. Under the same operating conditions without

AlCl3·6H2O (i.e., Fenton process), the melanoidin and total HCA degradation

efficiencies were slightly lower, at 63% and 47% respectively.

A predicted HCA degradation of 42% under the worst case experiment at a

higher pH (i.e., pH 6.0) shows that the modified Fenton process is heavily dependent

on pH. Interestingly, lower dosages of the reagents were only required under the

worst case conditions due to deactivation of reagents at higher pH levels (pH ≥ 5.50).

202

Degradation of compounds does not necessarily imply that a mixture is

decolourised. Parameter optimisation and model verification results for the

decolourisation model on melanoidin/HCA mixtures in sugar solutions are shown in

Table 6.6. The experimental values are in consistency with the predicted values based

on the reduced quadratic model. The low error in the experimental and predicted

values and reasonably high desirability values (≥ 0.825) indicate good agreement of

the results.


decolourisation of synthetic juice mixtures containing melanoidin

(2,000 mg/L), HCA (200 mg/L) and sucrose (15% (w/w)) at 35 °C;

and model verification.*

Experiments

Optimum Fenton Only Worst Case

pH 5.3 5.3 4.5

FeSO4·7H2O (mg/L) 289 289 400

AlCl3·6H2O (mg/L) 322 0 350

Decolourisation (%) 43 (42) 24 (25) –109 (–113)

Desirability 0.825 0.851 0.936



RSD was < 5.0%.

It is obvious that in the worst case experiment, higher dosages of FeSO4·7H2O

and AlCl3·6H2O would result in colour formation. At the optimum working pH of

5.3, using 289 mg/L FeSO4·7H2O and 322 mg/L AlCl3·6H2O, 43% decolourisation

was achieved. The predicted melanoidin and total HCA degradation under the

optimum decolourisation experiments were 62% and 47%, respectively. A significant

decrease in decolourisation performance was observed under the same conditions in

the absence of AlCl3·6H2O with only 24% decolourisation achieved. Therefore, the

modified Fenton process shows promise as the Fenton process is essential for the

203

breakdown of colour and colour precursor compounds, while the presence of Al(III)

aids in colour removal.

6.4 Summary

The works presented in this chapter extends the investigations outlined in the

previous chapters to evaluate the effect of the Fenton process on the degradation of

melanoidins, including HCA degradation and colour removal, by modifying the

Fenton process with the addition of Al(III). Changing the independent variables to be

more closely aligned to sugar cane factory processing conditions reduced the

complexity of the statistical analyses required.

A modified Fenton oxidation process where Al(III) is used to promote the

oxidation process is effective in the degradation and decolourisation of synthetic

sugar solutions containing a synthetic melanoidin and HCAs (viz., CaA, pCoA and

FeA). Ferrous iron does not remove colour but it is essential for the breakdown of the

melanoidin and HCAs. Also, Al(III) aids in the removal of the oxidation products and

colour. Decolourisation is best achieved with an increased dosage of Al(III). Despite

degradation of HCA with Fe(II), higher dosages would result in increased colour.

Lower dosages of Fe(II) combined with higher dosages of Al(III) are suitable for the

effective reduction of colour and the degradation of melanoidins and HCAs. Higher

dosages of Fe(II) and Al(III) are much to be avoided as they actually increase the

solution colour. Such addition to factory juices must be tightly controlled otherwise

the process would be counterproductive.

204

References

Cornard, J.-P., Caudron, A., & Merlin, J.-C. (2006). UV–visible and synchronous

fluorescence spectroscopic investigations of the complexation of Al(III) with

caffeic acid, in aqueous low acidic medium. Polyhedron, 25, 2215-2222.

Cortez, S., Teixeira, P., Oliveira, R., & Mota, M. (2011). Evaluation of Fenton and

ozone-based advanced oxidation processes as mature landfill leachate pre-

treatments. Journal of Environmental Management, 92, 749-755.

Dwyer, J., Griffiths, P., & Lant, P. (2009). Simultaneous colour and DON removal

from sewage treatment plant effluent: Alum coagulation of melanoidin. Water

Research, 43, 553-561.

Exley, C. (2004). The pro-oxidant activity of aluminum. Free Radical Biology and

Medicine, 36(3), 380-387.

Lapouge, C., & Cornard, J.-P. (2007). Reaction pathways involved in the mechanism

of AlIII chelation with caffeic acid: Catechol and carboxylic functions

competition. ChemPhysChem, 8(3), 473-479.

Ruipérez, F., Mujika, J. I., Ugalde, J. M., Exley, C., & Lopez, X. (2012). Pro-oxidant

activity of aluminum: Promoting the Fenton reaction by reducing Fe(III) to

Fe(II). Journal of Inorganic Biochemistry, 117(0), 118-123.

Shore, M., Broughton, N. W., Dutton, J. V., & Sissons, A. (1984). Factors affecting

white sugar colour. Sugar Technology Reviews, 12, 1-99.

Westerhoff, P., Chen, W., & Esparza, M. (2001). Fluorescence analysis of a standard

fulvic acid and tertiary treated wastewater. Journal of Environmental Quality,

30(6), 2037-2046.

205

CHAPTER 7

Evaluation of the Fenton and Fenton-like

Processes for the Removal of Colour from

Factory Sugar Cane Juice

7.1 Introduction..................................................................................... 205



7.2.2 Specification of Samples...................................................... 206

7.2.3 Decolourisation Procedure................................................. 207

7.2.4 Preparation of Flocculants................................................. 207

7.2.5 Preparation of Lime Saccharate......................................... 208

7.2.6 Clarification Procedure...................................................... 208

7.2.7 Turbidity Measurements...................................................... 209

7.2.8 Sucrose, Dry Substance and Purity Measurements............. 210

7.2.9 Reducing Sugars Composition Analyses............................. 210


Measurements......................................................................

210

7.2.11 Inorganic Ion Composition Analyses.................................. 211


7.3.1 First Decolourisation Trials................................................ 211

7.3.2 Second Decolourisation Trials............................................ 215

7.3.3 Economic Considerations.................................................... 222

7.4 Summary.......................................................................................... 222

206

7.1 Introduction

The works presented in Chapters 4 and 6 showed that the Fenton and modified

Fenton oxidation processes are capable of effectively decolourising and degrading

colourant and colour precursor compounds in aqueous and dilute sucrose solutions

(≤ 15% (w/w)). In this present study, the effects of the Fenton and modified Fenton

processes on the decolourisation of factory sugar cane juice were investigated.

On-site clarification trials were undertaken to investigate the effectiveness of

the Fenton oxidation process and variants of this process to remove colour from sugar

cane juice. Sugar cane juice colour is usually measured at pH 7.0 but additional

information about the nature of the colourants present may be obtained at pH 4.0 and

9.0. As previously stated in Section 2.4, colour measured at pH 4.0 suggests the

presence of HMW colourants, while colour at pH 9.0 is essentially due to the presence

of colour precursor, phenolic and flavonoid compounds. The results obtained from

these tests are reported in this chapter.



All chemicals, solvents and reagents were obtained in their purest form from

the suppliers as described in the previous chapters or as otherwise stated. Ferric

chloride (anhydrous) was purchased from Sigma-Aldrich (St. Louis, MO, USA).

Magnafloc LT27 flocculant (degree of hydrolysis (DH) of 27%; MW of 18 × 106 Da)

was obtained from Chemiplas Australia (Robina, QLD, Australia). Magnafloc LT340

flocculant (DH of 40%; MW of 18 × 106 Da) was obtained from TD Chemicals (East

Melbourne, VIC, Australia).

207

7.2.2 Specification of Samples

Sugar cane juice from the No. 2 mill was obtained from the processing lines at

Tully Sugar Mill (Tully, QLD, Australia) and Isis Central Sugar Mill (Childers, QLD,

Australia). Juices from the MJ and PJ process streams were also obtained from Isis

Central Sugar Mill. All juices were obtained as composites during the crushing

season in 2012. In total, four juices (2 × No. 2 mill juices, 1 × MJ and 1 × PJ) were

treated. The following analyses of the four juice samples are unrelated and not

comparable. However, the results obtained provide an insight on the levels of colour

present in each juice type before and after treatment with the Fenton and modified

Fenton processes.

7.2.3 Preparation of Flocculants

Solutions of flocculants (0.5% (w/v)) were best prepared by dispersing and

dissolving the flocculant powder in Milli-Q water (adjusted to pH 8.0–8.5 using

0.1 M NaOH) under gentle stirring at a low shear rate (50 rpm) for 3 h. The powders

were added at a rate which allowed good dispersion to ensure each flocculant particle

is hydrated to prevent agglomeration. Flocculant solutions were stored at 4.0 °C.

These solutions were diluted further to 0.1% (w/v) before being added to hot limed

sugar cane juice.

7.2.4 Preparation of Lime Saccharate

Lime saccharate used for juice clarification was obtained directly from the

factory of trial (viz., Tully Sugar Mill or Isis Central Sugar Mill). The mixture of lime

saccharate typically consists of 100 g of 20% (w/w) calcium oxide solution and 100 g

of 68 °Bx factory syrup.

208

7.2.5 Decolourisation Procedure

The procedure for the Fenton oxidation process with Al(III) on sugar cane

juices is similar to that described in Section 6.2.3. In each run, a predetermined

amount of FeSO4·7H2O or FeCl3, AlCl3·6H2O and H2O2 solutions were added to the

reaction vessel containing factory juice to achieve a final volume of ca. 1.0 L, while

maintaining the working Fenton molar ratio (Fe(II)/H2O2) at 1:15. The concentrations

of the reagents were chosen based on the previous research reported in Chapter 6.

After 2 min of oxidative treatment, the treated juice was immediately subject to

clarification.

7.2.6 Clarification Procedure

Clarification experiments were conducted in a heated and illuminated

clarification test kit designed by SRI (Brisbane, QLD, Australia) as shown in Figure

7.1. Each tube is of 1.0 L capacity with dimension of 460 × 55 mm i.d. The method

of clarification was simple defecation and typically involved liming to pH 7.8 at

76 °C, followed by boiling and settling in the settling tubes. A flocculant dosage

equivalent of 3 mg/kg of juice was applied prior to settling in the tubes. The

flocculant used for the Tully Sugar Mill trials was Magnafloc LT27, which is the

flocculant used at Tully Sugar Mill. Magnafloc LT340 was used for the trials at Isis

Central Sugar Mill, because of the experiences at Tully Sugar Mill. The initial

settling rate (cm/min) was obtained from the graphical analysis of the initial linear

slope and can be calculated as:

Settling Rate =

Initial Juice Level´40

100

æ

èçö

ø÷- Mud Level at 0.5min ´

40

100

æ

èçö

ø÷

0.5min

(7.1)

As the floc aggregates were unstirred, the mud heights were not indicative of

values obtained in commercial clarifier.

209

7.2.7 Turbidity Measurements

Absorbance measurements were conducted spectrophotometrically at 900 nm

(A900) on a GBC Scientific Cintra 40 double-beam UV/Vis spectrophotometer using

cells of 1.0 cm path length. The resulting turbidity of each sample was calculated as:

Turbidity (TU) = 100´ A900

(7.2)

Figure 7.1 Sugar Research Institute designed batch settling kit.

210

7.2.8 Sucrose, Dry Substance and Purity Measurements

The apparent sucrose content in juice (i.e., pol) was calculated by double

polarisation measurements performed on a Schmidt Haensch Polartronic Universal

digital polarimeter (Berlin, Germany) according to a standard procedure adapted from

BSES (2001a). Juice samples were clarified with lead acetate, followed by pol

measurements of the filtered clarified solutions (before and after sucrose inversion

with HCl. The pol expressed as % (w/w) is calculated from the change in polarisation

between the plain and inverted sugar solutions.

The dry substance procedure used to determine water and/or total solids in

juice is also adapted from a procedure by the BSES (2001b). Coiled strips of

Whatman No. 4 chromatography paper (600 × 50 mm) were saturated in juice and

dried in vacuo at < 7 kPa for 12 h in an oven at 65 °C. The loss of sample after drying

indicates the amount of water in the juice sample. The purity of juice samples is

expressed as a percentage of pol on DS as shown in Equation 7.3.

polPurity (%) = 100

DS

(7.3)

7.2.9 Reducing Sugars Composition Analyses

Reducing sugar contents in the reaction mixtures were monitored by HPAEC-

PAD. Sample preparation and the operating procedure for the chromatographic

system are identical to that described in Section 4.2.5.

7.2.10 Colour, Refractive Index and Total Soluble Solids Measurements

Sample preparation and the operating procedures for the determination of

colour, RI and TSS in juice samples are identical to those described in Section 3.2.6.

211

7.2.11 Inorganic Ion Composition Analyses

Inorganic ion composition analyses were performed on a Varian Vista-MPX

simultaneous inductively coupled plasma-optical emission spectrometer (ICP-OES)

with megapixel charge coupled device detection (Mulgrave, VIC, Australia). To

reduce the interference of the organic sugar matrix, samples were diluted to a sucrose

concentration of 2.0% (w/w). The measurements were conducted in duplicate. The

operating parameters listed in Table 7.1 were applied for all ICP-OES measurements.

Table 7.1 Operating parameters for ICP-OES analyses.

RF generator 40 MHz

Power 1.25 kW

Plasma flow 13.5 L/min

Auxiliary 0.75 L/min

Nebuliser flow 0.75 L/min

Viewing height 5 mm

Emission lines (nm) Na 589.592 Mg 279.553 Al 396.152

Si 251.611 P 213.618 S 181.972

K 769.897 Ca 317.933 Fe 238.204


7.3.1 First Decolourisation Trials

Clarification Results

The clarification of juice was assessed based on three clarification

performance parameters as shown in Table 7.2; turbidity, settling rate and mud height.

Juice turbidity values for all tests were higher (9.7–18 TU) than the control

(9.2 TU). The settling rate of the flocs for all tests was extremely slow. This may be

due to the presence of high starch levels as no incubation was carried out for the

naturally occurring α–amylase enzymes to break down the starch (Bruijn and

Jennings, 1968). The settling rates of the flocs formed in the juices treated via the

Fenton oxidation process were slightly lower (0.8–6.4 cm/min) than the value

212

obtained with the control (7.2 cm/min). Higher Fe(II) content reduced the settling

rate of the floc particles. In addition, higher dosages of the Fenton’s reagent (Tests 2

and 4) increased mud height levels by up to 58%. This is possibly due to the lower

density of mud particles.

Table 7.2 Clarification performance results on clarified No. 2 mill juices

from the Tully Sugar Mill trials.*

Dosage (mM)

Test Fe(II) H2O2 Al(III) Turbidity

(TU)

Settling rate

(cm/min)

MH†

(%)

Untreated 0 0 0 – – –

Control 0 0 0 9.2 7.2 12

Test 1 0.28 4.22 0 12 4.0 11

Test 2 0.50 7.50 0 18 2.4 14

Test 3 0.28 4.22 0.093 18 6.4 13

Test 4 0.50 7.50 0.093 9.7 0.8 19

*% RSD was < 5.0%. †MH (Mud Height)

Inorganic Ion Composition Results

Inorganic ion analysis was conducted on both untreated and treated juices

(Table 7.3). Higher dosages of Fenton’s reagent carried out in Tests 2 and 4 increased

residual Fe levels in clarified juice. However, the addition of Al(III) to the higher

Fenton dosage (Test 4) reduced the level of Fe by 20%. Despite the same Al(III)

dosage applied to both tests, the higher Fenton dosage (Test 4) showed a lower Al

level by 50% than the lower Fenton dosage (Test 3).

The addition of Fe(II) as FeSO4·7H2O contributed to the increase in S levels in

all tests conducted. Both FeSO4·7H2O and AlCl3·6H2O are acidic in nature and

increased the amount of lime saccharate used to reach the pH set point for

clarification. This resulted in increases with residual soluble Ca levels in the clarified

juices. More soluble Ca implies higher fouling rates in the evaporator vessels, which

213

is highly undesirable. More interestingly though is the reduction in P levels in Test 4.

This may be due to Fe(II)/Fe(III) and Al(III) ions reacting with free phosphate ions in

juice to form a precipitate. This is unlikely and may simply be due to the formation of

calcium phosphate precipitates as these precipitates have a lower solubility than

Fe(II), Fe(III) and Al(III) phosphates.

Table 7.3 Inorganic ion composition results on clarified No. 2 mill juices

from the Tully Sugar Mill trials.*

Concentration (mg/kg on TSS)

Test Na Mg Al Si P S K Ca Fe

Untreated 8 1,040 290 710 1,940 839 7,100 957 108

Control 29 792 19 333 458 875 7,190 1,980 19

Test 1 32 859 23 359 500 1,020 7,830 2,280 62

Test 2 29 888 22 326 494 1,690 7,190 3,030 258

Test 3 33 898 69 352 523 1,020 7,840 2,840 66

Test 4 38 1,040 35 359 346 1,920 8,460 3,970 205

*% RSD was < 5.0%

Colour Results

Table 7.4 shows the colour results obtained from the Tully Sugar Mill trials on

No. 2 mill juice. The results show that in the normal clarification process (i.e., the

control) the juice colour at pH 7.0 reduced by 24%, but little effects were observed at

pH 4.0 (–7.8%) and pH 9.0 (5.3%). The use of the Fenton and modified Fenton

processes reduced juice colour at pH 7.0 to a similar extent as the control. There was

a significant drop in juice colour at pH 9.0 using both Fenton and modified Fenton

processes with Al(III). The higher Fenton dosage (Test 2) achieved a reduction of

37% at pH 9.0 compared to the control. The addition of Al(III) (Test 4) also

significantly decreased the colour content at pH 9.0 by up to 42% and also slightly

decreased colour at pH 4 by ≤ 1.0%. The drop in colour at pH 9.0 is attributable to

the Fenton process degrading phenolic and flavonoid compounds.

214

The IVs of both untreated and treated juices as shown in Table 7.4 were

between 6.0 and 12 which are attributable to monomeric colourants such as natural

cane pigments (viz., flavonoids) (cf. Table 2.1). During normal clarification, a small

decrease in the IV of the control relative to the untreated No. 2 mill juice was

observed. This shows that a small percentage of factory produced colourants such as

polymers from HADPs and browning reactions involving phenolic compounds were

formed, hence an increase in colour at pH 4.0 (Paton, 1992). However, clarification

also removed some of the LMW colourants associated with pH 9.0. The IVs of the

treated juices were lower than the control (IV 6.0–8.4) indicating a lower presence of

LMW colourants. This confirms that the Fenton, Fenton-like and modified Fenton

processes are removing colourants and related compounds associated with pH 9.0.

Table 7.4 Colour results on clarified No. 2 mill juices from the Tully Sugar

Mill trials.*

Colour (IU)

Test pH 4.0 pH 7.0 pH 9.0 IV

Untreated 5,640 21,400 66,500 12

Control 6,080 16,400 63,000 10

Test 1 6,440 17,500 53,800 8.4

Test 2 6,950 17,400 41,700 6.0

Test 3 6,910 16,600 53,300 8.0

Test 4 5,560 16,600 38,500 7.0

*% RSD was < 5.0%

In summary, the Fenton process with Al(III) showed the best result with

respect to colour reduction. However, the observations for the poor settling rate of the

particles (apart from the likely presence of high starch levels) is probably due to the

flocculant (i.e., Magnafloc LT27) being incapable of bridging the flocs closely

together to increase the floc density and subsequently enhance sedimentation. The

lack of light bonds between the floc particles is possibly due to the composition of the

treated juices. The presence of residual Fe(II), Fe(III) and Al(III) cations in the

215

treated juice may have reduced the effectiveness of the flocculant to form tightly

bound floc structures. It was hypothesised that an alternative flocculant with higher

anionicity may improve the clarification performance of the treated juices.

7.3.2 Second Decolourisation Trials

Clarification Results

As previous results in Table 7.2 indicated that treating juices with iron and

aluminium salts would impact on the settling rate of the flocs, hence a flocculant with

high anionicity and high molecular weight was selected (Madsen and Day, 2010).

The flocculant Magnafloc LT340 was chosen for these trials in place of Magnafloc

LT27.

Table 7.5 shows the clarification performance results of No. 2 mill, MJ and PJ

obtained from the trials conducted at Isis Central Sugar Mill. In addition to the

modified Fenton process (Fe(II)/Al(III)/H2O2), Fe(III) was trialled in place of Fe(II)

as it has been previously reported that Fe(III) can be readily used as a chelant and

oxidant to create flocs for effective juice clarification (Madsen and Day, 2010).

Excellent clarified juice turbidity values (3–9 TU) were obtained with primary

juice treated with both modified Fenton and Fenton-like processes, though the

turbidity of the control was more pronouced. For this type of juice, the modified

Fenton process with Fe(II) gave a lower turbidity (4.6 TU) than the modified Fenton-

like process containing Fe(III) (9.4 TU). The reverse trend was observed for the

turbidity values of the clarified juices obtained from No. 2 mill juice and MJ. The

turbidity values obtained with Fe(II) (Tests 5 and 7) in place of Fe(III) were

unacceptably high. The reasons for these observations are not known, although

Fe(III) is known to be a more effective coagulant than Fe(II) (Rivas et al., 2002).

The sizes of the flocs formed by visual assessment with both modified Fenton

and Fenton-like processes were smaller than those formed by the normal clarification

process and the resulting settling rates were extremely slow (≤ 3.2 cm/min).

Reasonable mud heights (except Test 9) were obtained for the different types of juices

with the different Fenton treatments (Table 7.5).

216

Table 7.5 Clarification performance results on clarified factory juices from

the Isis Central Sugar Mill trials.*

Dosage (mM)

Test Fe(II) Fe(III) H2O2 Al(III) Turbidity

(TU)

Settling rate

(cm/min)

MH†

(%)

No. 2 Mill juice

Untreated 0 0 0 0 – – –

Control 0 0 0 0 9.0 33 19

Test 5 0.50 0 7.5 0.093 40 0.8 9.0

Test 6 0 0.92 7.5 0.093 15 8.0 17

Mixed juice

Untreated 0 0 0 0 – – –

Control 0 0 0 0 3.8 18 29

Test 7 0.50 0 7.5 0.093 21 3.2 27

Test 8 0 0.92 7.5 0.093 3.4 20 28

Primary juice

Untreated 0 0 0 0 – – –

Control 0 0 0 0 3.1 42 19

Test 9 0.50 0 7.5 0.093 4.6 0.8 25

Test 10 0 0.92 7.5 0.093 9.4 3.2 21

*% RSD was < 5.0%. †MH (Mud Height)

Inorganic Ion Composition Results

Table 7.6 shows the inorganic ion composition of the clarified juices. The

trends in the proportions of the inorganic ion concentrations were similar for both

modified Fenton and Fenton-like processes on each type of factory juice tested.

Interestingly, the modified Fenton-like process with Fe(III) (Tests 6 and 8)

significantly produced less residual Al and Fe than the modified Fenton process with

Fe(II) (Tests 5 and 7). However, the treatment of PJ with Fe(III) (Test 10) compared

to Fe(II) (Test 9) resulted in improved Fe removal but increased Al content.

217

This may simply be due to the lower turbidity (except Test 10 for Al) obtained

with Fe(III) treated juices, and as a consequence of the type and nature of the calcium

phosphate flocs formed. This is reflected in the lower levels of P (except for Test 10)

and Ca obtained for these treated juices. Therefore, it is presumed that the modified

Fenton-like process with Fe(III) does not interfere in the precipitation of calcium

phosphate to the same extent as the Fe(II) treated juices. Additions of Fe(III) (Tests

6, 8 and 10) appear not to boost the S content, are better with Ca content and have less

residual Fe content than that of Fe(II) additions (Tests 5, 7 and 9).

Table 7.6 Inorganic ion composition results clarified factory juices from


Concentration (mg/kg on TSS)

Test Na Mg Al Si P S K Ca Fe

No.2 Mill juice

Untreated 44 694 38 188 706 1,050 8,000 341 45

Control 75 533 5 130 272 1,090 7,720 663 9

Test 5 92 640 49 128 302 1,740 8,840 1,510 267

Test 6 61 459 36 82 247 929 7,770 1,000 68

Mixed juice

Untreated 18 719 11 79 676 1,370 5,970 568 9

Control 26 676 2 169 115 1,760 6,350 878 2

Test 7 25 659 11 94 152 2,030 6,300 1,230 67

Test 8 43 809 7 125 132 1,990 7,350 1,180 18

Primary juice

Untreated 108 758 8 133 600 1,330 6,920 917 7

Control 133 741 3 170 133 1,560 7,110 1,260 4

Test 9 136 728 8 136 112 2,080 7,120 1,680 70

Test 10 129 694 19 145 194 1,450 7,180 1,530 33

*% RSD was < 5.0%

218

The addition of Fe(II) as FeSO4·7H2O contributed to the increase in S levels in

juices treated with Fe(II) (Table 7.6). The salts FeSO4·7H2O, FeCl3 and AlCl3·6H2O

are acidic in nature and their addition to juice reduced the pH. This resulted in an

increase in the amount of lime saccharate added to reach the pH set point for

clarification. The effect of this is an increase in the soluble Ca content (Table 7.6) of

the juices treated with these reagents compared to the control experiments where these

reagents were not used. The modified Fenton and Fenton-like processes show

reductions in Si content in all juice types compared to the control (Table 7.6). This is

due to the formation of insoluble aluminium-silicate compounds during clarification

(Thai et al., 2012).

Purity and Reducing Sugars Results

During raw sugar manufacture, sucrose loss through inversion to glucose and

fructose, and degradation to organic acids are minimised by working within the

desired pH ranges. The values in Table 7.7 indicate that no significant changes to the

purity levels occurred in the clarified juice due to the treatment using both the Fenton

and modified Fenton processes when compared with the control. However, there

were increases in the levels of glucose (≤ 14%) and fructose (≤ 9.0%) indicating some

sucrose degradation.

219

Table 7.7 Purity and reducing sugar results on clarified factory juices from


Reducing Sugar Content % (w/w)

Test Glucose Fructose Purity (%)

No. 2 Mill juice

Untreated 0.09 0.07 83.8

Control 0.04 0.04 90.5

Test 5 0.27 0.25 89.5

Test 6 0.26 0.23 89.9

Mixed juice

Untreated 0.21 0.35 82.3

Control 0.06 0.05 92.8

Test 7 0.26 0.23 90.9

Test 8 0.27 0.24 92.8

Primary juice

Untreated 0.09 0.09 86.9

Control 0.07 0.09 92.3

Test 9 0.21 0.18 91.5

Test 10 0.18 0.16 91.9

*% RSD was < 5.0%

Colour Results

Table 7.8 shows, significant colour present in the clarified juice of No. 2 mill

juice obtained via the normal clarification process compared to the juices clarified

from MJ and PJ (Curtin and Paton, 1980). There are increases in the colour measured

at pH 4.0 (37–45%) and pH 7.0 (11–21%) for No. 2 mill juice treated by the modified

Fenton and Fenton-like processes relative to the control (Table 7.8). The colours in

clarified MJs and PJs measured at pH 4.0 and pH 7.0 respectively follow similar

trends as the clarified No. 2 mill juice. However, the colour levels measured at pH

9.0 for the clarified No. 2 mill juice decreased significantly for the modified Fenton

process with Fe(II) (Test 5) and with Fe(III) (Test 6) by 42% and 38% respectively

220

relative to the control. There were also decreases for the colour measured at pH 9.0

for clarified PJs (≤ 26%) using the modified Fenton and Fenton-like processes,

however the magnitude of decolourisation was reduced. There was only a marginal

decrease in colour at pH 9.0 with the treated MJs (Tests 7 and 8). It is presumed that

some non-sucrose impurities present in higher proportions in MJ may have interfered

with the oxidation processes thereby preventing the degradation of colourants. The

IVs of all the treated juices were lower (4.6–5.9) when compared with the respective

control sample (6.5–12). This indicates and confirms the significant decreases in

LMW colourants in the treated juices using the modified Fenton and Fenton-like

processes.

Table 7.8 Colour results on clarified factory juices from the Isis Central

Sugar Mill trials.*

Colour (IU)

Test pH 4 pH 7 pH 9 IV

No. 2 Mill Juice

Untreated 9,120 18,400 72,400 8.0

Control 5,250 14,200 63,300 12

Test 5 7,200 15,800 36,600 5.1

Test 6 7,600 17,200 39,100 5.1

Mixed Juice

Untreated 3,560 8,830 22,300 6.3

Control 3,990 9,020 25,900 6.5

Test 7 4,230 10,800 23,800 5.6

Test 8 5,310 11,600 24,200 4.6

Primary Juice

Untreated 4,220 9,660 31,700 7.5

Control 4,000 8,620 33,700 8.4

Test 9 4,710 9,050 24,900 5.3

Test 10 4,490 10,300 26,400 5.9

*% RSD was < 5.0%

221

The modified Fenton-like process with Fe(III) (Tests 6, 8 and 10) show

slightly higher colour than the corresponding process with Fe(II) (Table 7.8). This is

attributable to the slower rate of H2O2 decomposition to the active •OH radical

necessary for the degradation of colourants, when the more stable Fe(III) is used in

place of Fe(II) (Sedlak and Andren, 1991; Pignatello, 1992; Arnold et al., 1995).

Also, as the total amount of iron in juice is approximately 10–20 ppm on juice (van

der Poel et al., 1998) and is present as Fe(III), the optimum working molar ratio of

Fe(III) and H2O2 was not used in these studies.

The modified Fenton and Fenton-like processes resulted in a decrease in

colour at pH 9.0, but this decrease was offset by an increase in colour at pH 4.0 and

pH 7.0. As stated previously, a decrease in colour at pH 9.0 indicates a reduction in

flavonoids and phenolics, and these colourants have a major influence on raw sugar

colour inclusion into the crystals of raw sugar (Smith and Paton, 1985; Clarke et al.,

1986; Riffer, 1988; Davis, 2001). However, the major contributors to the impurities

and colour in the raw sugar lie in the molasses layer of the surface of the crystals.

The crystallisation of raw sugar, if ideal, rejects all impurities from the crystal

structure. In practice, impurities are trapped (layered in) within the crystalline

structure, co-crystallised with sucrose into the crystal lattice. As well, some

impurities are present in gross molasses inclusions within the crystal, and a good deal

are left as the molasses or syrup film around the crystals. Little firm data is available

on these aspects, particularly in relation to the relative magnitude of the layering

effect and the impurity inclusion effect.

The inherent colour in the modified Fenton and Fenton-like processes on its

own has minimal absorbance at 420 nm where colour is measured. However, in the

presence of a colourant or colour precursor compound (e.g., CaA), the colours of the

clarified juices are inflated (Riffer, 1988). It should therefore be noted that although

reduction in colour at pH 7.0 (the usual measurement) of the clarified juice were not

obtained with the modified Fenton and Fenton-like processes, significant colour

reduction may have been realised if clarified juices were furthered processed to raw

sugar, based on the aforementioned observations. Such an investigation should be

undertaken in future research work.

222

7.3.3 Economic Considerations

Bulk quantities of FeSO4·7H2O, AlCl3·6H2O and H2O2 reagents required for

the modified Fenton oxidation process are all available commercially. The pricing of

these additives, exclusive of GST and delivered to the metropolitan area of Brisbane,

Australia are listed in Table 7.9.

Table 7.9 Prices of additives in bulk quantities used for the modified Fenton

process.

Chemical Company Origin Price (AUD $/t)

FeSO4·7H2O Swancorp Australia $350

AlCl3·6H2O Shanghai Smart Chemicals China $450

H2O2, 50% (w/v) Solvay Interox Australia $1,050

The approximate cost of the best treatment, conducted in this study

(i.e., Test 7), for one tonne of factory cane juice at FeSO4·7H2O (2.49 mM),

AlCl3·6H2O (0.83 mM) and H2O2 (7.5 mM) is $A0.24, $A0.06 and $A0.42,

respectively (i.e., total of $A0.72/t of juice). The additional costs for the uses of

increased lime saccharate and the flocculant (i.e., Magnafloc LT340) needed for all

Fenton-mediated processes used as well as possible sucrose losses have not been

taken into account. As approximately eight tonnes of Australian MJ is required to

produce one tonne of raw sugar, it would cost ca. $A5.76/t of sugar. The costs of

reagents can be further reduced if bulk quantities are sourced.

7.4 Summary

This study was aimed at the decolourisation of factory cane juice using

Fenton, Fenton-like and modified Fenton oxidation processes. Results have shown

that the modified Fenton oxidation process (i.e., Fe(II)/Al(III)/H2O2) significantly

reduced colour measured at pH 9.0 (associated with LMW colourants and colour

precursors) for clarified juices of No. 2 Mill and PJs. However, the modified Fenton

process did not reduce colour levels measured at pH 4.0 (associated with HMW

223

colourants) and at pH 7.0. The results obtained from the second decolourisation trials

conducted at Isis Central Sugar Mill also confirm the results obtained from the initial

trials carried out at Tully Sugar Mill. Problems associated with small floc size and

slow settling of flocs should be addressed in future studies. Furthermore, treated

juices should be used to produce sugar, in order to establish whether the Fenton and

modified Fenton processes can produce low colour sugar. If low colour sugar can be

produced, it will be necessary in a future project to investigate ways to minimise

sucrose degradation using Fenton oxidation technologies.

224

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Muller (Eds.), Sugar Technology Reviews (Vol. 12, pp. 117-141). Amsterdam:

Elsevier.

Thai, C. C. D., Bakir, H., & Doherty, W. O. S. (2012). Insights to the clarification of

sugar cane juice expressed from sugar cane stalk and trash. Journal of

Agricultural and Food Chemistry, 60, 2916-2923.

van der Poel, P. W., Schiweek, H., & Schwartz, T. (1998). Sugar Technology: Beet

and Cane Manufacture. Berlin: Verlag Dr. Albert Bartens KG.

226

227

CHAPTER 8

Conclusions and Future Aspects

8.1 Findings of the Thesis...................................................................... 228

8.2 Recommendations for Future Work.............................................. 231

228

8.1 Findings of the Thesis

The sugar industry is constantly looking at ways to cost effectively remove

impurity loadings in sugar process streams as these impurities impact on the colour

formed in raw sugar. Besides, the industry is concerned with progressive colour

formation of raw sugar during storage due to oxidation of phenolic compounds,

present in these impurities. This thesis has presented a detailed study on the

degradation of HCAs and also the decolourisation of sugar cane juice.

The phenolic acid and colour composition of factory cane juices processed by

Australian sugar factories was investigated. Phenolic compounds are of interest, as

they are known to be natural colour precursors. These compounds can react with

other organic and inorganic components in juice through enzymatic and non-

enzymatic reactions to produce highly coloured polymeric compounds that contribute

considerably to raw sugar colour.

Amongst the juice extracts of FEJ and PJ process streams, fifteen phenolic

compounds, HMF and furfural were quantified. Changes to the conventional

procedure by dissolving the dried extracts in methanol in place of water, showed an

overall improved response to phenolic acids and revealed the presence of flavonoids.

The chromatographic results reveal that the phenolic acids; CaA, pCoA and FeA were

of the highest concentrations, which are classed as HCAs, present in juice extracts

from Australian factory FEJ and PJ. Moreover, the concentrations of phenolic acids

in burnt cane were twice as much as those obtained in whole crop cane. This is

probably due to the thermal decomposition of HMW phenolics (viz., lignin,

polyphenols) during cane burning.

The colour analyses showed that juice expressed from whole crop harvested

cane has significantly higher colour than juices (11,400–20,000 IU) expressed from

burnt harvested cane (10,400–12700 IU) attributable to the higher amounts of

impurities and natural colourants entering the manufacturing process.

A detailed investigation on the degradation of CaA was conducted. A

quadratic polynomial model was obtained for CaA degradation through the use of

CCD and RSM, and indicated that initial sucrose and CaA concentration significantly

decreased the amount of CaA degraded. Numeric optimisation based on the

229

desirability function was used to determine the optimum process parameters. It

showed that in water at 35 °C, 80% of CaA was degraded at pH 5.5 using 0.72 mM

Fe(II) and 0.44 mM H2O2. However, for a synthetic sugar solution (13% (w/w)

sucrose), under processing conditions similar to that of MJ, only 61% of CaA was

degraded.

The Fenton process was also used to degrade phenolic compounds in synthetic

juice mixtures containing HCAs (viz., CaA, pCoA and FeA), sucrose and water.

Numerous models were developed and validated to predict the degradation of HCAs

through the use of RSM. The models were not only used to predict the optimum

conditions for the degradation of the HCAs but to also understand and probe the

effects of each significant parameter and their interaction with one another on the

degradation of HCAs. Under the optimised conditions for a 200 mg/L initial HCA

mixture concentration, the degradation efficiencies of the mixture in water and sugar

solutions (i.e., 13% (w/w) sucrose) were 77% and 57% respectively.

Sucrose was the most influential parameter that significantly lowered the

degradation efficiencies of the HCAs in the Fenton process. The behaviour of CaA

degradation in the composite system is different from that of pCoA and FeA, possibly

due to its ability to form complexes with Fe(III), as its aromatic ring is highly

activated with the presence of two hydroxyl groups.

Attempts were made to identify and quantify some of the reaction products

from the Fenton oxidation of HCAs by means of LC/MS, HPAEC-PAD, HPIEC and

GC/MS. Mechanistic oxidation pathways were proposed with support from previous

works in the literature. The presence of phenolic aldehydes and aliphatic carboxylic

acids suggest that the Fenton process is oxidising and breaking down the HCAs.

However, the formation of oligomeric products from the oxidative coupling of

cinnamoyl radicals indicates that the Fenton process is also polymerising some of the

oxidised products.

Modifications to the Fenton process were made by either adding AlCl3·6H2O

to the mixture prior to oxidation and/or replacing Fe(II) with Fe(III) (i.e., Fenton-like

process). The oxidation performance of these additives was evaluated on both

complex synthetic juice systems (containing a synthetic melanoidin, HCAs, sucrose

and water) and factory sugar cane juice.

230

In a synthetic juice solution consisting of sucrose (15% (w/w)), the HCAs

(200 mg/L) and a synthetic glucose-glycine melanoidin (2,000 mg/L), the addition of

AlCl3·6H2O in the modified Fenton process degraded the melanoidin and the HCAs

by approximately 69% and 53% respectively. However, AlCl3·6H2O did not play a

significant role in degradation because the Fenton process on its own (i.e., without

AlCl3·6H2O), under the same conditions resulted in 63% and 47% degradation,

respectively. On the other hand, the addition of AlCl3·6H2O played a significant role

in the removal of colour with up to 43% decolourisation at pH 5.3 using 289 mg/L

FeSO4·7H2O, 107 mg/L H2O2 and 322 mg/L AlCl3·6H2O. The Fenton process on its

own, under the same conditions only gave 24% decolourisation.

In factory sugar cane juice, the modified Fenton oxidation process (i.e.,

Fe(II)/Al(III)/H2O2) showed a decrease in colour at pH 9.0 (≤ 42%) for various

factory juices (No. 2 mill, mixed and primary) with minimal sucrose loss. However,

there were increases in colour at pH 4.0 (≤ 45%) and pH 7.0 (≤ 21%) under the same

conditions. Moreover, it is noted that colour measured at pH 9.0 is readily transferred

to the sugar crystal relative to the colour measured at pH 4.0 and pH 7.0, and so some

colour reduction will be realised if these clarified juices were processed to raw sugar.

Overall, the studies conducted throughout this project have shown that the

Fenton and modified Fenton processes are capable of degrading and decolourising

sugar process streams with minimal losses of sucrose.

A preliminary minimum cost calculation indicated that the modified Fenton

processes were found to be reasonably inexpensive for decolourisation of sugar

process streams. Under the optimum working conditions of the modified Fenton

process (i.e., 0.50 mM Fe(II), 0.093 mM Al(III) and 7.5 mM H2O2), effective

decolourisation of factory cane juice at pH 9.0 can be achieved at a cost of $A0.72/t

of juice.

231

8.2 Recommendations for Future Work

A number of suggestions are proposed for future work, based on the research

findings of this thesis. The advantages of the use of the modified Fenton process in

the sugar manufacturing process include its simplicity, its non-specific oxidation

property and the use of inexpensive equipment. Also, the sludge that is produced has

the potential to remove colourants and other impurities (including proteins and

polysaccharides) improving the quality of sugar process streams.

Clarification of Treated Juice

Problems associated with small size and slow settling of flocs need to be

addressed to achieve optimum decolourisation and prevent any carryover of colour in

downstream processes attributable to the finer particles that are not separable during

sedimentation. A suitable coagulating agent such as an anionic polyacrylamide or

polydiallyldimethylammonium chloride for the binding and precipitating for these

flocs needs to be looked into for the effective clarification of juice using the modified

Fenton process.

The sludge produced during Fenton oxidation means that it must be used

before clarification to remove that sludge. It cannot be used on evaporator syrup

unless the treated syrup then undergoes a flotation-type process in order to completely

remove the residual sludge. Otherwise high turbidity will be carried through into the

product sugar, and this is not acceptable.

Raw Sugar Production

In order to determine the extent of Fenton and modified Fenton oxidation as

viable decolourisation processes in sugar production, treated juices should be used to

produce raw sugar. If low colour sugar can be produced, it will be necessary to

investigate ways to minimise sucrose degradation using Fenton oxidation

technologies.

232

Toxicity Measurements

Apart from the measurement of colour, other measurements such as chemical

oxygen demand, total polyphenolic content, total aromaticity and toxicity. Evaluation

of toxicity is important for assessing the impact of oxidised compounds produced

from the Fenton process of food process streams.

Degradation Products

A thorough investigation in probing the oxidation of HCAs and other phenolic

compounds in solution is still required. It is important to understand the degradation

mechanism of these compounds via the Fenton process in order to propose detailed

mechanistic pathways for the conversion of the starting organic materials to their

mineralisation products (i.e., CO2 and H2O). One solution that could be used to better

determine and quantify the reaction products is to initially isolate and purify them first

via preparative HPLC. The combined use of various spectroscopic techniques

including UV/Vis, NMR and FTIR will assist in the characterisation and structure

elucidation of these compounds, especially oligomeric products which are not

available commercially. Changes to the voltages applied during GC/MS and LC/MS

analyses can also be envisioned, to improve fragmentation and assist in the

determination of unknown reaction products of phenolic compounds via the Fenton

process or any other catalytic oxidation process.

Other Oxidants and Catalysts

It is recommended to investigate the oxidative performance of the Fenton

process by using other oxidants in place of H2O2 such as organic hydroperoxides and

peroxy acids or by using other iron-based materials as catalysts. Recently, significant

attention has been paid to the use of cheap heterogeneous catalysts in place of the

typical homogeneous Fe(II) catalysts to overcome the high amounts of iron-

containing sludge formed after oxidation. Bulk iron-containing materials (e.g., red

mud from alumina processing) and natural iron-containing clay minerals (e.g.,

goethite, hematite or magnetite) should be used as Fenton catalysts as they require

233

minimal catalyst preparation and activation. Another approach is to incorporate

aluminium and/or iron onto activated carbons, clays, polymers and zeolites. These

heterogeneous catalysts would not only assist in the degradation of the target

compounds but provide synergies in assisting in the clarification and removal of

intermediate and by-products.

234

235

APPENDICES

236

Table A1.1 Experimental design and results for % CaA degradation

(i.e., Design 1).

Test CaA

(mM)

Sucrose

% (w/w)

pH [Fe(II)]

(mM)

[H2O2]

(mM)

Temp.

(°C)

Time

(s)

Degradation

(%)

1 0.28 34.00 6.5 0.72 2.21 95 120 48

2 1.11 17.00 5.0 0.45 6.62 65 65 52

3 1.11 34.00 3.5 0.72 11.03 95 120 21

4 0.28 34.00 3.5 0.72 2.21 95 10 33

5 0.695 17.00 5.0 0.45 6.62 65 65 40

6 0.695 17.00 5.0 0.45 6.62 65 65 40

7 1.11 34.00 6.5 0.72 11.03 35 10 42

8 0.28 0.00 6.5 0.18 11.03 95 120 67

9 1.11 0.00 6.5 0.18 11.03 95 10 23

10 0.28 0.00 3.5 0.18 2.21 35 10 33

11 1.11 34.00 3.5 0.18 2.21 95 120 16

12 0.695 0.00 5.0 0.45 6.62 65 65 77

13 0.28 0.00 3.5 0.18 11.03 35 120 76

14 0.28 34.00 3.5 0.72 2.21 35 10 33

15 1.11 0.00 3.5 0.72 2.21 95 120 32

16 1.11 34.00 6.5 0.18 2.21 95 120 18

17 0.28 0.00 6.5 0.72 11.03 35 120 80

18 0.695 17.00 5.0 0.45 6.62 35 65 45

19 1.11 0.00 6.5 0.18 11.03 35 120 38

20 0.28 34.00 6.5 0.18 11.03 35 10 46

21 1.11 34.00 3.5 0.72 11.03 35 120 45

22 1.11 0.00 6.5 0.72 2.21 35 120 64

23 0.695 17.00 5.0 0.45 6.62 65 65 40

24 0.695 17.00 5.0 0.45 6.62 65 10 40

25 0.28 0.00 6.5 0.72 11.03 95 120 87

26 0.28 34.00 6.5 0.72 11.03 35 120 34

27 1.11 0.00 6.5 0.72 11.03 35 120 90

28 1.11 0.00 3.5 0.18 2.21 35 120 24

29 0.28 34.00 6.5 0.72 11.03 35 10 28

237

30 0.28 0.00 3.5 0.18 2.21 35 120 65

31 0.28 34.00 3.5 0.72 11.03 35 10 26

32 0.28 0.00 3.5 0.18 11.03 95 10 75

33 1.11 0.00 3.5 0.18 2.21 35 10 1

34 1.11 34.00 6.5 0.72 2.21 35 120 26

35 0.28 0.00 6.5 0.18 2.21 35 120 81

36 0.28 34.00 3.5 0.18 2.21 95 10 32

37 0.28 0.00 6.5 0.72 2.21 35 120 81

38 0.28 34.00 3.5 0.72 11.03 95 10 7

39 1.11 0.00 6.5 0.72 2.21 35 10 35

40 0.695 17.00 5.0 0.45 11.03 65 65 27

41 0.28 34.00 6.5 0.72 2.21 95 10 43

42 1.11 34.00 3.5 0.72 2.21 35 10 18

43 0.695 17.00 5.0 0.45 6.62 65 65 40

44 0.695 17.00 5.0 0.45 6.62 65 65 40

45 1.11 0.00 6.5 0.18 11.03 35 10 8

46 1.11 0.00 3.5 0.18 11.03 95 120 77

47 0.695 17.00 3.5 0.45 6.62 65 65 19

48 0.28 34.00 6.5 0.72 11.03 95 120 43

49 0.28 34.00 3.5 0.18 2.21 35 10 27

50 1.11 0.00 3.5 0.72 11.03 35 120 67

51 1.11 34.00 6.5 0.18 2.21 35 10 0

52 1.11 0.00 6.5 0.18 2.21 35 120 23

53 0.28 34.00 6.5 0.18 11.03 95 120 19

54 1.11 34.00 6.5 0.18 2.21 35 120 16

55 1.11 0.00 3.5 0.72 11.03 35 10 50

56 1.11 34.00 6.5 0.18 11.03 95 10 14

57 1.11 34.00 6.5 0.72 2.21 95 120 31

58 0.28 0.00 6.5 0.72 11.03 95 10 83

59 1.11 34.00 6.5 0.72 11.03 95 10 48

60 0.695 17.00 6.5 0.45 6.62 65 65 38

61 1.11 34.00 6.5 0.72 11.03 95 120 46

62 1.11 34.00 3.5 0.72 2.21 95 10 33

238

63 1.11 0.00 3.5 0.18 11.03 35 10 11

64 0.28 0.00 3.5 0.72 2.21 95 120 80

65 0.28 34.00 6.5 0.18 2.21 35 120 56

66 1.11 34.00 6.5 0.18 2.21 95 10 3

67 0.28 0.00 6.5 0.18 11.03 35 120 81

68 0.695 17.00 5.0 0.45 6.62 65 65 40

69 0.28 34.00 6.5 0.18 2.21 35 10 30

70 1.11 0.00 6.5 0.18 2.21 95 10 9

71 0.28 34.00 3.5 0.18 11.03 35 120 35

72 0.28 34.00 6.5 0.18 2.21 95 120 27

73 0.28 34.00 6.5 0.18 2.21 95 10 25

74 0.28 0.00 3.5 0.18 2.21 95 10 72

75 1.11 0.00 3.5 0.18 11.03 35 120 60

76 1.11 0.00 3.5 0.72 2.21 95 10 32

77 0.695 34.00 5.0 0.45 6.62 65 65 35

78 1.11 0.00 6.5 0.18 2.21 35 10 3

79 0.28 34.00 6.5 0.72 11.03 95 10 34

80 0.28 34.00 3.5 0.72 2.21 95 120 33

81 0.28 17.00 5.0 0.45 6.62 65 65 39

82 0.695 17.00 5.0 0.45 6.62 65 120 40

83 0.28 0.00 6.5 0.18 2.21 95 10 72

84 1.11 0.00 3.5 0.72 11.03 95 120 66

85 0.28 0.00 6.5 0.72 11.03 35 10 72

86 0.28 0.00 6.5 0.72 2.21 95 10 81

87 1.11 34.00 3.5 0.18 11.03 35 10 15

88 1.11 34.00 3.5 0.72 2.21 95 120 36

89 0.695 17.00 5.0 0.45 6.62 65 65 40

90 1.11 0.00 3.5 0.72 2.21 35 10 21

91 1.11 0.00 6.5 0.18 2.21 95 120 28

92 0.28 34.00 3.5 0.18 11.03 95 120 29

93 0.28 0.00 6.5 0.18 2.21 95 120 82

94 1.11 0.00 6.5 0.72 11.03 35 10 67

95 1.11 0.00 6.5 0.72 11.03 95 10 77

239

96 1.11 34.00 3.5 0.72 2.21 35 120 23

97 1.11 34.00 3.5 0.72 11.03 35 10 38

98 0.28 34.00 3.5 0.18 2.21 35 120 33

99 0.28 34.00 3.5 0.18 11.03 95 10 28

100 0.695 17.00 5.0 0.45 6.62 65 65 40

101 0.695 17.00 5.0 0.45 6.62 65 65 40

102 0.28 0.00 6.5 0.72 2.21 35 10 67

103 0.28 0.00 3.5 0.72 2.21 95 10 79

104 1.11 0.00 6.5 0.72 2.21 95 120 57

105 0.695 17.00 5.0 0.45 6.62 95 65 50

106 0.695 17.00 5.0 0.45 6.62 65 65 40

107 0.28 0.00 3.5 0.72 2.21 35 120 67

108 1.11 34.00 3.5 0.18 11.03 95 120 41

109 0.28 0.00 3.5 0.72 11.03 35 120 75

110 0.695 17.00 5.0 0.72 6.62 65 65 24

111 1.11 0.00 6.5 0.72 2.21 95 10 57

112 0.28 34.00 6.5 0.72 2.21 35 10 43

113 0.28 0.00 6.5 0.72 2.21 95 120 85

114 1.11 0.00 6.5 0.18 11.03 95 120 53

115 1.11 34.00 3.5 0.18 11.03 95 10 35

116 0.28 0.00 3.5 0.18 2.21 95 120 77

117 0.28 34.00 3.5 0.72 2.21 35 120 35

118 1.11 34.00 6.5 0.18 11.03 95 120 39

119 1.11 34.00 6.5 0.18 11.03 35 120 32

120 0.28 34.00 3.5 0.72 11.03 95 120 28

121 1.11 34.00 3.5 0.18 2.21 35 120 12

122 0.695 17.00 5.0 0.45 2.21 65 65 30

123 0.28 34.00 6.5 0.72 2.21 35 120 45

124 1.11 34.00 3.5 0.72 11.03 95 10 17

125 1.11 34.00 3.5 0.18 2.21 95 10 15

126 0.28 0.00 3.5 0.18 11.03 35 10 52

127 1.11 0.00 3.5 0.18 11.03 95 10 70

128 0.28 0.00 3.5 0.72 11.03 35 10 59

240

129 0.28 34.00 3.5 0.18 2.21 95 120 32

130 0.28 0.00 6.5 0.18 2.21 35 10 35

131 0.28 0.00 3.5 0.72 11.03 95 10 95

132 0.28 34.00 3.5 0.18 11.03 35 10 31

133 0.28 34.00 3.5 0.72 11.03 35 120 32

134 0.28 0.00 3.5 0.18 11.03 95 120 86

135 1.11 34.00 6.5 0.72 2.21 35 10 19

136 1.11 34.00 6.5 0.72 2.21 95 10 30

137 1.11 0.00 3.5 0.18 2.21 95 120 43

138 1.11 0.00 6.5 0.72 11.03 95 120 81

139 0.695 17.00 5.0 0.18 6.62 65 65 44

140 0.28 0.00 3.5 0.72 2.21 35 10 49

141 0.28 0.00 6.5 0.18 11.03 35 10 72

142 0.28 0.00 6.5 0.18 11.03 95 10 48

143 1.11 34.00 6.5 0.72 11.03 35 120 63

144 1.11 34.00 3.5 0.18 2.21 35 10 5

145 1.11 0.00 3.5 0.72 2.21 35 120 30

146 1.11 34.00 3.5 0.18 11.03 35 120 43

147 1.11 0.00 3.5 0.18 2.21 95 10 42

148 0.28 0.00 3.5 0.72 11.03 95 120 97

149 1.11 0.00 3.5 0.72 11.03 95 10 66

150 1.11 34.00 6.5 0.18 11.03 35 10 16

151 0.28 34.00 6.5 0.18 11.03 95 10 21

152 0.28 34.00 6.5 0.18 11.03 35 120 51

241

Table A1.2 Sucrose and reducing sugar results on selected tests at t = 2 min

(i.e., Design 1).*

Sugar Content % (w/w) Sugar Content % (w/w)

Test Glucose Fructose Sucrose Test Glucose Fructose Sucrose

1 0.01 0.02 33.98 1B 0.00 0.00 33.98

3 0.53 0.25 34.01 3B 0.00 0.00 34.02

11 0.05 0.03 33.99 11B 0.00 0.00 34.00

16 0.00 0.00 34.05 16B 0.00 0.00 34.05

21 0.24 0.21 33.98 21B 0.00 0.00 33.98

26 0.02 0.03 34.00 26B 0.00 0.00 34.00

34 0.01 0.01 34.05 34B 0.00 0.00 34.05

48 0.28 0.24 34.12 48B 0.00 0.00 34.11

53 0.02 0.02 34.07 53B 0.00 0.00 34.08

54 0.00 0.00 34.05 54B 0.00 0.00 34.05

57 0.02 0.03 34.02 57B 0.00 0.00 34.02

61 0.05 0.07 33.96 61B 0.00 0.00 33.96

65 0.02 0.01 33.98 65B 0.00 0.00 33.98

71 0.00 0.01 33.97 71B 0.00 0.00 33.97

72 0.03 0.02 34.02 72B 0.00 0.00 34.02

80 0.02 0.04 34.00 80B 0.00 0.00 34.00

82 0.14 0.12 16.98 82B 0.00 0.00 16.98

88 0.01 0.03 34.02 88B 0.00 0.00 34.02

92 0.10 0.11 34.05 92B 0.00 0.00 34.05

96 0.01 0.00 34.00 96B 0.00 0.00 34.01

98 0.00 0.00 34.02 98B 0.00 0.00 34.02

108 0.03 0.02 34.10 108B 0.00 0.00 34.10

117 0.00 0.00 34.01 117B 0.00 0.00 34.01

118 0.17 0.20 34.02 118B 0.00 0.00 34.02

119 0.00 0.00 34.01 119B 0.00 0.00 34.01

120 0.10 0.11 33.98 120B 0.00 0.00 33.98

121 0.00 0.00 34.00 121B 0.00 0.00 34.00

123 0.05 0.04 34.01 123B 0.00 0.00 34.01

129 0.01 0.02 33.97 129B 0.00 0.00 33.97

242

133 0.01 0.00 33.98 133B 0.00 0.00 33.99

143 0.01 0.01 33.95 143B 0.00 0.00 33.95

146 0.02 0.02 34.01 146B 0.00 0.00 34.01

152 0.00 0.01 34.03 152B 0.00 0.00 34.04

*Tests denoted with B indicate blank tests (i.e., t = 0 min)

243

Table A1.3 Experimental design and results for % CaA, % pCoA, % FeA and

% total HCA degradation (i.e., Design 2).

Degradation (%)

Test Total HCA

(mg/L)

Sucrose

% (w/w)

pH Temp.

(°C)

CaA pCoA FeA Total

HCA

1 200 7.50 5.0 38 85 37 43 55

2 155 11.25 4.8 31 95 42 44 60

3 110 7.50 5.0 38 90 52 53 65

4 65 3.75 5.3 31 71 65 70 69

5 110 15.00 5.0 38 94 46 43 61

6 110 7.50 5.0 25 96 53 55 68

7 155 3.75 5.3 44 74 60 62 65

8 65 11.25 4.8 31 96 45 45 62

9 110 7.50 5.0 38 90 48 50 63

10 155 11.25 4.8 31 95 46 48 63

11 155 11.25 4.8 44 95 40 44 59

12 110 7.50 5.5 38 39 57 56 51

13 65 11.25 4.8 31 96 47 50 64

14 110 0.00 5.0 38 61 77 79 73

15 20 7.50 5.0 38 94 62 57 71

16 110 0.00 5.0 38 60 76 78 72

17 65 3.75 4.8 31 95 66 69 77

18 110 7.50 5.0 38 91 52 50 64

19 155 3.75 4.8 31 93 58 59 70

20 110 15.00 5.0 38 93 – – 52

21 155 11.25 5.3 31 79 48 51 59

22 65 11.25 4.8 44 97 33 37 56

23 155 3.75 4.8 44 94 52 56 67

24 155 11.25 4.8 44 95 41 43 60

25 200 7.50 5.0 38 85 43 47 58

26 65 3.75 5.3 44 70 70 74 71

27 155 3.75 4.8 44 93 56 57 69

28 155 3.75 5.3 31 82 67 66 –

244

29 110 7.50 4.5 38 97 39 43 59

30 65 3.75 4.8 44 96 57 65 72

31 65 3.75 4.8 31 95 58 63 72

32 65 3.75 4.8 44 95 55 64 72

33 155 3.75 4.8 31 93 55 54 67

34 65 11.25 5.3 31 80 49 – 66

35 110 7.50 5.0 25 95 48 53 65

36 65 11.25 4.8 44 96 35 40 57

37 65 11.25 5.3 44 91 46 45 60

38 65 11.25 5.3 44 88 52 49 63

39 155 11.25 5.3 44 87 49 49 62

40 110 7.50 5.0 50 90 51 57 66

41 65 3.75 5.3 31 67 63 68 66

42 110 7.50 5.5 38 45 49 54 50

43 155 3.75 5.3 31 80 60 60 67

44 20 7.50 5.0 38 97 47 53 65

45 65 11.25 5.3 31 81 48 48 59

46 110 7.50 4.5 38 97 34 39 57

47 155 11.25 5.3 31 82 53 57 64

48 110 7.50 5.0 38 90 52 53 65

49 110 7.50 5.0 50 90 50 52 64

50 110 7.50 5.0 38 90 50 50 63

51 155 3.75 5.3 44 72 60 63 65

52 65 3.75 5.3 44 73 72 75 73

53 155 11.25 5.3 44 85 53 52 63

54 110 7.50 5.0 38 90 48 50 63

245

Table A1.4 Sucrose and reducing sugar results at t = 2 min (i.e., Design 2).*



1 0.00 0.00 7.47 1B 0.00 0.00 7.47

2 0.00 0.00 11.20 2B 0.00 0.00 11.20

3 0.00 0.00 7.53 3B 0.00 0.00 7.53

4 0.00 0.01 3.81 4B 0.00 0.00 3.82

5 0.00 0.00 15.01 5BB 0.00 0.00 15.01

6 0.00 0.00 7.55 6B 0.00 0.00 7.55

7 0.00 0.01 3.75 7B 0.00 0.00 3.76

8 0.00 0.00 11.30 8B 0.00 0.00 11.30

9 0.00 0.01 7.84 9B 0.00 0.00 7.85

10 0.00 0.00 11.27 10B 0.00 0.00 11.27

11 0.00 0.00 11.22 11B 0.00 0.00 11.22

12 0.01 0.00 7.90 12B 0.00 0.00 7.90

13 0.00 0.00 11.28 13B 0.00 0.00 11.28

14 0.00 0.00 0.00 14B 0.00 0.00 0.00

15 0.01 0.00 7.55 15B 0.00 0.00 7.55

16 0.00 0.00 0.00 16B 0.00 0.00 0.00

17 0.01 0.01 3.75 17B 0.00 0.00 3.76

18 0.00 0.00 7.49 18B 0.00 0.00 7.49

19 0.00 0.01 3.80 19B 0.00 0.00 3.81

20 0.00 0.00 14.99 20B 0.00 0.00 14.99

21 0.00 0.00 11.26 21B 0.00 0.00 11.26

22 0.00 0.00 11.20 22B 0.00 0.00 11.20

23 0.00 0.01 3.82 23B 0.00 0.00 3.82

24 0.00 0.00 11.31 24B 0.00 0.00 11.31

25 0.01 0.00 7.60 25B 0.00 0.00 7.60

26 0.01 0.01 3.78 26B 0.00 0.00 3.79

27 0.01 0.00 3.82 27B 0.00 0.00 3.82

28 0.00 0.01 3.72 28B 0.00 0.00 3.72

29 0.00 0.00 7.52 29B 0.00 0.00 7.52

30 0.01 0.00 3.72 30B 0.00 0.00 3.73

246

31 0.00 0.01 3.73 31B 0.00 0.00 3.74

32 0.02 0.01 3.76 32B 0.00 0.00 3.77

33 0.00 0.00 3.79 33B 0.00 0.00 3.79

34 0.00 0.00 11.20 34B 0.00 0.00 11.20

35 0.00 0.00 7.42 35B 0.00 0.00 7.42

36 0.00 0.00 11.24 36B 0.00 0.00 11.24

37 0.01 0.01 11.20 37B 0.00 0.00 11.20

38 0.00 0.00 11.22 38B 0.00 0.00 11.22

39 0.00 0.00 11.27 39B 0.00 0.00 11.27

40 0.00 0.00 7.52 40B 0.00 0.00 7.52

41 0.00 0.00 3.76 41B 0.00 0.00 3.76

42 0.00 0.00 7.50 42B 0.00 0.00 7.51

43 0.01 0.00 3.74 43B 0.00 0.00 3.76

44 0.00 0.00 7.51 44B 0.00 0.00 7.51

45 0.01 0.00 11.24 45B 0.00 0.00 11.24

46 0.00 0.00 7.47 46B 0.00 0.00 7.47

47 0.00 0.00 11.22 47B 0.00 0.00 11.22

48 0.00 0.00 7.41 48B 0.00 0.00 7.41

49 0.00 0.01 7.50 49B 0.00 0.00 7.50

50 0.00 0.00 7.52 50B 0.00 0.00 7.52

51 0.00 0.01 3.73 51B 0.00 0.00 3.74

52 0.00 0.01 3.74 52B 0.00 0.00 3.74

53 0.00 0.00 11.27 53B 0.00 0.00 11.27

54 0.01 0.00 7.60 54B 0.00 0.00 7.61


247


calculations of CaA.

SPARTAN '10 MECHANICS PROGRAM: PC/x86 1.1.0

Frequency Calculation

Adjusted 1 (out of 63) low frequency modes

Reason for exit: Successful completion

Mechanics CPU Time : .14

Mechanics Wall Time: .29

SPARTAN '10 Quantum Mechanics Program: (PC/x86) Release 1.1.0v4

WARNING: Parallel not implemented with this method

Job type: Geometry optimization.

Method: RB3LYP

Basis set: 6-31G(D)

Number of shells: 68

Number of basis functions: 211

Multiplicity: 1

SCF model:

A restricted hybrid HF-DFT SCF calculation will be

performed using Pulay DIIS + Geometric Direct Minimization

Solvation: water [SM8]

Optimization:

Step Energy Max Grad. Max Dist.

1 -648.682524 0.011054 0.031973

2 -648.683422 0.002900 0.011599

3 -648.683483 0.001034 0.002655

4 -648.683490 0.000395 0.001440

5 -648.683490 0.000140 0.000430

248


Quantum Calculation CPU Time : 13:44.31

Atomic Charges:

Electrostatic Mulliken Natural

1 H1 : +0.212 +0.146 +0.248

2 C1 : -0.374 -0.258 -0.248

3 C4 : -0.243 -0.170 -0.302

4 C2 : +0.208 +0.164 -0.112

5 C6 : +0.308 +0.261 +0.277

6 C5 : +0.200 +0.392 +0.291

7 C3 : -0.244 -0.229 -0.203

8 H3 : +0.167 +0.165 +0.250

9 H4 : +0.176 +0.195 +0.258

10 C7 : -0.143 -0.163 -0.120

11 H8 : +0.150 +0.163 +0.248

12 C8 : -0.310 -0.188 -0.371

13 H9 : +0.174 +0.175 +0.247

14 C9 : +0.663 +0.461 +0.785

15 O1 : -0.575 -0.497 -0.668

16 O2 : -0.588 -0.607 -0.718

17 H10 : +0.439 +0.445 +0.517

18 O3 : -0.567 -0.680 -0.710

19 H2 : +0.453 +0.453 +0.515

20 O4 : -0.582 -0.699 -0.712

21 H6 : +0.476 +0.470 +0.530

249

Bond Orders Mulliken

1 C1 H1 : 0.907

2 C1 C4 : 0.073

3 C1 C2 : 1.338

4 C1 C6 : 1.472

5 C1 C8 : 0.031

6 C1 O3 : 0.045

7 C4 C5 : 1.394

8 C4 C3 : 1.421

9 C4 H4 : 0.901

10 C4 O4 : 0.037

11 C2 C5 : 0.070

12 C2 C3 : 1.374

13 C2 C7 : 1.104

14 C6 C5 : 1.281

15 C6 C3 : 0.066

16 C6 O3 : 0.987

17 C6 O4 : 0.046

18 C5 C8 : 0.028

19 C5 O4 : 0.889

20 C3 H3 : 0.915

21 C3 C7 : 0.034

22 C3 C8 : 0.050

23 C7 H8 : 0.910

24 C7 C8 : 1.704

25 C7 C9 : 0.045

26 C7 O1 : 0.057

27 C8 H9 : 0.906

28 C8 C9 : 1.043

250

29 C9 O1 : 1.945

30 C9 O2 : 1.015

31 O1 O2 : 0.055

32 O1 H10 : 0.026

33 O2 H10 : 0.737

34 O3 H2 : 0.721

35 O4 H2 : 0.041

36 O4 H6 : 0.727


Properties CPU Time : 1.14

Properties Wall Time: 1.35

251


calculations of pCoA.










Method: RB3LYP

Basis set: 6-31G(D)



Multiplicity: 1

SCF model:




Optimization:


1 -573.459367 0.014428 0.082246

2 -573.462031 0.004680 0.029876

3 -573.462253 0.001063 0.003240

4 -573.462261 0.000340 0.001653

5 -573.462262 0.000109 0.000325

252



Quantum Calculation Wall Time: 11:59.19

Atomic Charges:


1 H1 : +0.161 +0.168 +0.247

2 C1 : -0.212 -0.178 -0.179

3 C4 : -0.335 -0.171 -0.314

4 C2 : +0.211 +0.148 -0.130

5 C6 : -0.234 -0.190 -0.288

6 C5 : +0.410 +0.352 +0.347

7 C3 : -0.161 -0.219 -0.178

8 H3 : +0.152 +0.170 +0.250

9 H4 : +0.180 +0.189 +0.255

10 C7 : -0.191 -0.159 -0.119

11 H8 : +0.158 +0.159 +0.246

12 C8 : -0.302 -0.186 -0.374

13 H9 : +0.182 +0.178 +0.248

14 C9 : +0.670 +0.453 +0.785

15 O1 : -0.576 -0.496 -0.669

16 O2 : -0.598 -0.607 -0.719

17 H10 : +0.441 +0.445 +0.517

18 O4 : -0.569 -0.670 -0.694

19 H6 : +0.440 +0.458 +0.515

20 H11 : +0.173 +0.157 +0.255

253


1 C1 H1 : 0.907

2 C1 C4 : 0.078

3 C1 C2 : 1.361

4 C1 C6 : 1.489

5 C1 C8 : 0.034

6 C4 C5 : 1.369

7 C4 C3 : 1.448

8 C4 H4 : 0.903

9 C4 O4 : 0.044

10 C2 C5 : 0.071

11 C2 C3 : 1.364

12 C2 C7 : 1.108

13 C6 C5 : 1.367

14 C6 C3 : 0.075

15 C6 O4 : 0.032

16 C6 H11 : 0.917

17 C5 C3 : 0.029

18 C5 C8 : 0.026

19 C5 O4 : 0.965

20 C3 H3 : 0.913

21 C3 C7 : 0.031

22 C3 C8 : 0.046

23 C7 H8 : 0.911

24 C7 C8 : 1.701

25 C7 C9 : 0.047

26 C7 O1 : 0.057

27 C8 H9 : 0.905

28 C8 C9 : 1.044

254

29 C9 O1 : 1.950

30 C9 O2 : 1.013

31 O1 O2 : 0.055

32 O1 H10 : 0.026

33 O2 H10 : 0.738

34 O4 H6 : 0.736




255


calculations of pCoA.










Method: RB3LYP

Basis set: 6-31G(D)



Multiplicity: 1

SCF model:




Optimization:


1 -687.885006 0.782278 0.172563

2 -687.946054 0.309605 0.069009

3 -687.962560 0.152032 0.158684

4 -687.967209 0.046289 0.101182

5 -687.974773 0.023712 0.037747

6 -687.975517 0.011028 0.013128

256

7 -687.975705 0.003954 0.003702

8 -687.975724 0.001689 0.002490

9 -687.975729 0.000477 0.001537

10 -687.975730 0.000136 0.000451



Quantum Calculation Wall Time: 36:02.80

Atomic Charges:


1 H1 : +0.217 +0.149 +0.248

2 C1 : -0.560 -0.264 -0.230

3 C4 : -0.299 -0.182 -0.300

4 C2 : +0.384 +0.169 -0.116

5 C6 : +0.405 +0.285 +0.268

6 C5 : +0.241 +0.375 +0.286

7 C3 : -0.304 -0.224 -0.204

8 H3 : +0.184 +0.167 +0.250

9 H4 : +0.194 +0.193 +0.255

10 C7 : -0.251 -0.162 -0.120

11 H8 : +0.165 +0.163 +0.247

12 C8 : -0.219 -0.186 -0.370

13 H9 : +0.170 +0.177 +0.248

14 C9 : +0.601 +0.461 +0.785

15 O1 : -0.555 -0.496 -0.668

16 O2 : -0.588 -0.607 -0.718

17 H10 : +0.447 +0.445 +0.517

18 O3 : -0.283 -0.559 -0.549

257

19 O4 : -0.561 -0.678 -0.700

20 H6 : +0.452 +0.463 +0.520

21 C10 : -0.373 -0.216 -0.313

22 H2 : +0.177 +0.174 +0.217

23 H5 : +0.177 +0.174 +0.217

24 H7 : +0.179 +0.178 +0.227


1 C1 H1 : 0.904

2 C1 C4 : 0.074

3 C1 C2 : 1.358

4 C1 C6 : 1.443

5 C1 C8 : 0.033

6 C1 O3 : 0.037

7 C4 C5 : 1.371

8 C4 C3 : 1.447

9 C4 H4 : 0.900

10 C4 O4 : 0.044

11 C2 C5 : 0.068

12 C2 C3 : 1.359

13 C2 C7 : 1.102

14 C6 C5 : 1.287

15 C6 C3 : 0.067

16 C6 O3 : 0.925

17 C6 O4 : 0.032

18 C5 C8 : 0.027

19 C5 O3 : 0.025

20 C5 O4 : 0.917

21 C3 H3 : 0.913

258

22 C3 C7 : 0.031

23 C3 C8 : 0.046

24 C7 H8 : 0.910

25 C7 C8 : 1.705

26 C7 C9 : 0.046

27 C7 O1 : 0.058

28 C8 H9 : 0.905

29 C8 C9 : 1.042

30 C9 O1 : 1.946

31 C9 O2 : 1.014

32 O1 O2 : 0.055

33 O1 H10 : 0.026

34 O2 H10 : 0.738

35 O3 C10 : 0.880

36 O4 H6 : 0.731

37 C10 H2 : 0.930

38 C10 H5 : 0.930

39 C10 H7 : 0.941




259

Table A2.4 Sucrose and reducing sugar results of Fenton-mediated reactions

of sucrose at t = 2 min.*



1 0.02 0.01 3.75 1B 0.00 0.00 3.76

2 0.01 0.01 7.50 2B 0.00 0.00 7.51

3 0.00 0.00 11.25 3B 0.00 0.00 11.24

4 0.00 0.00 15.01 4B 0.00 0.00 15.01


Figure A2.1 High-performance LC-DAD chromatograms (UV/Vis detection at

280 nm) of the HCA mixture subjected to Fenton oxidation at

2 min (pH 4.7, 25 °C).

-10

30

70

110

150

0 5 10 15 20 25

Ab

sorb

an

ce (

mA

U)


260

Figure A2.2 Total ion chromatogram (negative ion mode ESI-MS) of the HCA

mixture subjected to Fenton oxidation at 2 min (pH 4.7, 25 °C).

Figure A2.3 Gas chromatogram of a SPE extract of the HCA mixture

subjected to Fenton oxidation at 2 min (pH 4.7, 25 °C).

0

5

10

15

20

25

0 5 10 15 20 25

Inte

nsi

ty (

10

6 )


0.00

0.40

0.80

1.20

1.60

2.00

15 20 25 30 35

Inte

nsi

ty (

10

6 )


×

×

261


dimer product arising from the Fenton oxidation of FeA,

[M]– = 385.1 Da.


tetramer product arising from the Fenton oxidation of CaA,

[M]– = 715.2 Da.

0.0

2.0

4.0

6.0

100 300 500 700 900

Inte

nsi

ty (

10

6 )

Mass-to-Charge (m/z) Ratio

715.2

471.1

357.1

393.1

269.1

179.1

0.0

1.0

2.0

3.0

4.0

100 200 300 400

Inte

nsi

ty (

10

6 )

Mass-to-Charge (m/z) Ratio

385.1

341.1

297.1

249.0189.1

155.0

×

×

262

Table A3.1 Experimental design for % total HCA, % melanoidin degradation

and decolourisation.

Test Melanoidin

(mg/L)

Total HCA

(mg/L)

pH FeSO4·7H2O

(mM)

AlCl3·6H2O

(mM)

1 1500 150 5.63 0.85 0.41

2 1000 100 5.25 1.40 0.83

3 1000 100 6.00 1.40 0.83

4 500 50 4.88 1.94 0.41

5 1000 0 5.25 1.40 0.83

6 500 150 5.63 1.94 0.41

7 1500 150 4.88 1.94 0.41

8 1500 50 5.63 1.94 0.41

9 500 150 4.88 0.85 0.41

10 0 100 5.25 1.40 0.83

11 1500 50 4.88 0.85 0.41

12 1000 200 5.25 1.40 0.83

13 1000 100 5.25 1.40 0

14 1000 100 4.50 1.40 0.83

15 500 150 4.88 1.94 1.24

16 500 150 5.63 0.85 1.24

17 1500 50 4.88 1.94 1.24

18 1000 100 5.25 1.40 0.83

19 500 50 4.88 0.85 1.24

20 500 50 5.63 0.85 0.41

21 2000 100 5.25 1.40 0.83

22 1000 100 5.25 1.40 1.66

23 1000 100 5.25 1.40 0.83

24 1000 100 5.25 1.40 0.83

25 1500 50 5.63 0.85 1.24

26 1500 150 4.88 0.85 1.24

27 1000 100 5.25 0.31 0.83

28 1000 100 5.25 1.40 0.83

29 1000 100 5.25 1.40 0.83

263

30 500 50 5.63 1.94 1.24

31 1000 100 5.25 2.49 0.83

32 1500 150 5.63 1.94 1.24

264

Table A3.2 Results for % total HCA, % melanoidin degradation and

decolourisation.

Degradation (%)

Test Melanoidin Total HCA Decolourisation (%)

1 70 43 51

2 65 47 12

3 64 45 23

4 64 51 33

5 67 – 8

6 – 46 47

7 69 50 -42

8 64 51 14

9 63 – 18

10 – 50 –

11 71 – 6

12 66 48 28

13 66 47 34

14 – 49 –

15 74 46 -121

16 62 52 22

17 76 52 45

18 65 51 20

19 65 47 23

20 71 51 –

21 70 48 -3

22 64 46 27

23 66 48 –

24 71 47 8

25 64 40 34

26 63 48 25

27 65 – 42

28 65 49 -3

29 66 – 10

265

30 69 48 –

31 – 49 10

32 64 52 12

266

Figure A3.1 Normal probability plots of residuals for fitted model using

(a) melanoidin and (b) total HCA degradation data after power

transformation.

(a)

(b)

267

Figure A3.2 Box-Cox plots of (a) melanoidin and (b) total HCA degradation

data for the determination of the optimised power transformed

response surface models.

(a)

(b)

268

Figure A3.3 Plots of predicted response and experimental (actual) values for

the degradation (%) of (a) melanoidin and (b) total HCA.

(a)

(b)

269

Figure A3.4 Plot of predicted response and experimental (actual) values for

the decolourisation (%).

270

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