OPTIMISATION AND KINETIC MODELLING FOR THE PRODUCTION OF 5-

AMINOLEVULINIC ACID BY RHODOPSEUDOMONAS PALUSTRIS IN THE

SOLID STATE FERMENTATION

ONG PEI YING

A thesis submitted in fulfilment of the

requirements for the award of the degree of

Doctor of Philosophy (Bioprocess Engineering)

Faculty of Chemical & Energy Engineering

Universiti Teknologi Malaysia

MARCH 2017

iii

DEDICATION

To my beloved family for their love and support

iv

ACKNOWLEDGEMENT

I would like to express my deepest appreciation to my supervisor, Assoc. Prof.

Dr. Lee Chew Tin for her patience, persistent help and contribution of knowledge.

Her guidance and inspiring suggestions helped me in all the time of my PhD research

and thesis writing. I would also like to extend my appreciation to my co-supervisor,

Prof. Dr. Mohamad Roji Bin Sarmidi for his motivation and insightful comments

during my research. I would also like to express my special thanks to Assoc. Prof. Dr.

Emma Nehrenheim from Malardalen University for her support and guidance during

my one year research attachment in Sweden. My sincere thanks go to all the lecturers,

the technicians, research officers and staffs in Institute of bioproduct and

development in Universiti Teknologi Malaysia and Malardalen University who have

directly or indirectly assisted and support me during my experimental works,

modelling development and thesis writing. I would like to thanks to my wonderful

parents and family members for their love, caring and patient. Heartfelt thanks goes

to my future husband, Weng Hong for all your love and encourage. Without all these

supports and assistances, I would not have been able to accomplish my PhD study.

I would also like to express my gratitude to Ministry of Higher Education

(MOHE), MyBrain15 Program, Erasmus Mundus program for their financial

scholarship support. I would also like to acknowledge the Research University Grant

(RU) Vote No.00H73 and Fundamental Research Grant Scheme (FRGS) Vote

No.78206 by Universiti Teknologi Malaysia for their financial research support

throughout the duration of my study.

v

ABSTRACT

The approach of the bioprocess system engineering (BPSE) serves as a

systematic methodology to better understand the overall performance of complex

biological system through optimisation process and development of a compatible

macroscopic kinetic model. Based on the BPSE approach, the production of 5-

aminolevulinic acids (ALA) by Rhodopseudomonas palustris (Rp) via solid state

fermentation (SSF), using the palm empty fruit bunch as solid state medium was

studied. Optimisation studies were carried out using a full-factorial design and the

response surface methodology approach. A maximum ALA yield of 43.72 mg/kg

was achieved under the following optimum conditions: moisture content of 63.13 %,

incubation temperature of 30.3 °C, pH 7, inoculums density of 40 % (v/w), 3.64 mM

glycine and 23.03 mM succinic acid for 48 hours via SSF. Three mathematical

models including the Logistic, Gompertz and Luedeking-Piret models were proposed

and compared based on their goodness of curve-fitting to the SSF experimental data.

The Logistic model incorporated with Luedeking-Piret model was developed and

best represented (R2 >0.95) the underlying kinetic behaviour of the growth of Rp, the

formation of ALA and the consumption of substrates for the production of ALA by

Rp in SSF at the optimum condition. The computed kinetic parameters including the

maximum specific growth rate (µm= 0.232 h-1

) with the maximum Rp biomass

concentration (Xmax= 316.4 x 10-9

CFU.g-1

) for the modelling of Rp growth; the

growth-associated (α= 8.249 mg.kg-1

.h-1

) and non-growth associated (β = -1.660

mg.kg-1

.h-1

) coefficients for the modelling of ALA formation, and the Rp growth

associated and the ALA formation associated on substrate consumption coefficient

(YX/S = 0.132 and YP/S = 0.141) for the modelling of substrate consumption were

evaluated. These values were then validated between the predicted data and the

experimental data using the least square curve fitting analysis and the ordinary

differential equation solver (ODE45) using the Matlab software.

vi

ABSTRAK

Sistem kejuruteraan bioproses (BPSE) menyediakan metodologi yang

sistematik untuk lebih memahami prestasi keseluruhan dalam sistem biologi yang

kompleks melalui pengoptimuman dan pembentukan model kinetik makroskopik

yang sesuai. Berdasarkan kaedah sistem BPSE, penghasilan asid aminolevulinik

(ALA) daripada Rhodopseudomonas palustris (Rp) melalui proses fermentasi pepejal

(SSF) dengan menggunakan tandan kosong kelapa sawit sebagai bahan medium

pertumbuhan telah dikaji. Kajian pengoptimuman telah dilakukan dengan

menggunakan reka bentuk penuh faktorial dan pendekatan metodologi permukaan

gerak balas. Jumlah maksimum ALA sebanyak 43.72 mg/kg telah dicapai dalam

keadaan optimum seperti berikut: kandungan kelembapan 63.13 %, suhu inkubator

30.3 °C, pH 7, ketumpatan inokulum 40 % (v/w), glisina 3.64 mM dan asid suksinik

23.03 mM selama 48 jam melalui SSF. Tiga model matematik iaitu Logistik,

Gompertz dan Luedeking-Piret telah digunakan dan dibandingkan berdasarkan

kepada kepadanan lengkung data eksperimen SSF yang terbaik. Model Logistik

bergabung dengan model Luedeking-Piret telah dibentuk (R2 >0.95) untuk

menerangkan pertumbuhan mikrob Rp, penghasilan ALA dan penggunaan substrat

untuk menghasilkan ALA oleh Rp melalui SSF dalam keadaan yang optimum.

Parameter kinetik yang terlibat termasuklah kadar nilai maksimum tertentu (µm=

0.232 h-1

) dengan kepekatan biojisim maksimum Rp (Xmax= 316.4 x 10-9

CFU.g-1

)

untuk model pertumbuhan Rp; nilai pekali berkaitan dengan pertumbuhan (α= 8.249

mg.kg-1

.h-1

) dan nilai pekali yang tidak berkaitan dengan pertumbuhan (β = -1.660

mg.kg-1

.h-1

) untuk penghasilan ALA; pekali penggunaan substrat untuk pertumbuhan

Rp dan pekali penggunaan substrak untuk penghasilan ALA (YX/S = 0.132 and YP/S =

0.141) bagi model penggunaan substrat juga dinilai. Nilai-nilai ini ditentusahkan di

antara model yang diramalkan dengan data dari eksperimen menggunakan analisis

kepadanan lengkung kuasa dua terkecil dan penyelesai persamaan kebezaan biasa

(ODE45) dengan menggunakan perisian Matlab.

vii

TABLE OF CONTENT

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENTS iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES xii

LIST OF FIGURES xv

LIST OF ABBREVIATION xxi

LIST OF SYMBOL xxii

LIST OF APPENDICES xxiii

1 INTRODUCTION 1

1.1 Research Background 1

1.2 Problem Statement 3

1.3 Objective of the Study 5

1.4 Scopes of Study 6

1.5 Significant of the Study 7

2 LITERATURE REVIEW 9

2.1 Compost 10

2.2 Bioprocess System Engineering (BPSE) 11

2.3 Empty Fruit Bunch (EFB) 14

viii

2.4 Microbial Inoculants 17

2.5 Rhodopseudomonas palustris (Rp) 18

2.6 5-Aminolevulinic Acid (ALA) 21

2.6.1 The Application of ALA 24

2.6.2 The Biosynthetic Pathway of ALA 25

2.6.3 The Biochemistry of ALA 27

2.7 Quantitative analysis for ALA determination 28

2.7.1 Chemical Derivatisation for Colorimetric

Method 28

2.7.2 Pre-column Derivatisation for HPLC 29

2.8 Solid State Fermentation (SSF) 30

2.9 Optimisation of the Physiochemical Conditions In SSF

Process 34

2.9.1 Moisture Content 34

2.9.2 Temperature and pH 36

2.9.3 Supplementary of Nutrient Substrates 37

2.9.4 Incubation Time 39

2.10 Optimisation of the Production of ALA by Rp in SSF 41

2.10.1 Classical One-Factor-At-A-Time Method 41

2.10.2 Statistical Design of Experimental 42

2.10.2.1 Preliminary Screening with Full-

Factorial Design 43

2.10.2.2 Optimisation with Response Surface

Methodology (RSM) 44

2.10.3 Combination of the Optimisation Experimental

Design 45

2.11 Mathematical Modelling for the Biological Process 46

2.12 Kinetic model represented by the Ordinary Differential

Equations (ODE) 47

2.12.1 Logistic Model 50

2.12.2 Gompertz Model 51

2.12.3 Luedeking-Piret Model 53

2.13 MATLAB (MATrix LABoratory) 56

ix

2.14 ODE Solver using the 4th

Runge-Kutta Method 57

2.15 Estimation of the Kinetic Parameter Constants 58

3 METHODOLOGY 60

3.1 Empty fruit bunch (EFB) as Solid Support Medium 61

3.2 Chemicals 62

3.3 Stock Culture of Rhodopseudomonas palustris (Rp) 62

3.4 Starter culture preparation 63

3.5 Production of 5-Aminolevulinic Acid (ALA) in Solid

State Fermentation (SSF) 64

3.6 Optimisation for the production of ALA by Rp in SSF 65

3.7 Classical optimisation using One-Factor-at-a-Time

Method 66

3.7.1 The effect of the concentration of the basal

medium 66

3.7.2 The effect of incubation temperature 67

3.7.3 The effect of inoculums density level of Rp 67

3.7.4 The effect of moisture content of the EFB 67

3.7.5 The effect of Initial pH of the basal medium 68

3.7.6 The effect of incubation time 68

3.8 Statistical design for the optimisation study of the

experimental work 68

3.8.1 Preliminary screening Full-Factorial statistical

analysis using the Plackett-Burman design 69

3.8.2 Optimisation of the most significant variables

by Response Surface Methodology (RSM), the

Box-Behnken design 71

3.8.3 Optimisation of the concentration of

supplementary substrates using Box-Behnken

design 71

3.9 Kinetic Profile for the Development of Kinetic Model 72

3.10 Analytical Methods 73

3.10.1 Determination of Rp Cell Growth 73

x

3.10.2 Determination of the final pH of the fermented

EFB 73

3.10.3 Extraction of the ALA metabolite for

colorimetric analysis 74

3.10.4 Extraction of Glycine, Succinic Acid and ALA

for HPLC analysis 74

3.10.5 Quantification of 5-Aminolevulinic Acid

(ALA) 76

3.10.5.1 Determination of ALA using

colorimetric analysis 76

3.10.5.2 Determination of ALA using HPLC

analysis 78

3.10.6 Quantification of Glycine and Succinic Acid

using HPLC analysis 80

3.10.6.1 Determination of the total Glycine

content 80

3.10.6.2 Determination of the total Succinic

Acid content 83

3.11 Kinetic modelling for the production of ALA by Rp via

SSF under optimum condition 84

3.12 Kinetic and Modelling for the growth of Rp 87

3.13 Kinetic and Modelling for the formation of ALA 87

3.14 Kinetic and Modelling for substrate consumption 89

3.15 Unknown parameters estimation and model validation 92

3.15.1 Ordinary Differential Equation Solver (ODE45) 92

3.15.2 The Least Square Curve Fit (lsqcurvefit) 93

3.15.3 Model Validation using Coefficient of

Determination (R2) 93

4 RESULTS AND DISCUSSION 95

4.1 One-Factor-at-a-Time for the optimisation of the

production of ALA by Rp in SSF 96

xi

4.1.1 Effect of the basal medium concentration 97

4.1.2 Effect of the incubation temperature 98

4.1.3 Effect of the inoculum density of Rp 100

4.1.4 Effect of the moisture content 102

4.1.5 Effect of the initial pH 104

4.1.6 Effect of the incubation period 107

4.2 Statistical design of experiments for the optimization

of the ALA by Rp in SSF 109

4.2.1 Initial screening process using the Full-

Factorial method, Plackett-Burman design 110

4.2.2 Optimisation of the selected screened variables

using the Box-Behnken design 116

4.2.3 Optimisation of the concentration of

supplementary substrates 131

4.3 Kinetic models for the Production of ALA by Rp in

SSF under Optimised Condition 144

4.3.1 Kinetic profile and Modelling of Rp growth 146

4.3.2 Kinetic profile and Modelling of the formation

of ALA 152

4.3.3 Kinetic Profile and Modelling of the substrate

consumption 159

4.4 Validation of the selected model for the production of

ALA by Rp 165

5 CONCLUSION 168

5.1 Conclusion 168

5.2 Recommendation 170

REFERENCES 172

Appendices A-T 192-212

xii

LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 Utilisation of the Empty Fruit Bunch (EFB) for the

production of high value-added products via SSF 16

2.2 The production of 5-Aminolevulinic Acid (ALA) by

different microorganism. 23

2.3 Comparison of characteristics for the SSF and SmF process 31

2.4 Recent research studies from Year 2010-2015 for the

production of ALA by different microorganism with

different supplementation nutrients via SmF. 40

2.5 A summary of kinetic models used for the modelling of cell

growth, substrate consumption and product formation. 49

3.1 Fermentation variables and supplementary nutrients to be

monitored in the Plackett-Burman design for the production

of ALA in SSF by Rp. 70

3.2 The Plackett-Burman design matrix for the preliminary

screening of seven independent variables on the production

of ALA by Rp in SSF. 70

3.3 Selected variables and assigned codes for the Box-Behnken

design matrix. 71

3.4 Selected supplementary substrate variables and assigned

codes for the Box-Behnken design matrix 72

4.1 The effect of the basal medium concentration (1, 2, 3, 4, 5

folds concentration) for the production of ALA in SSF by

Rp. 97

4.2 The effect of the incubation temperature (20, 25, 30, 35,

xiii

45°C) for the production of ALA in SSF by Rp. 98

4.3 The effect of the inoculum density (20, 30, 40, 50, 60%) for

the production of ALA in SSF by Rp 101

4.4 The effect of the moisture content (40, 50, 60, 70, 80%) for

the production of ALA by Rp in SSF. 103

4.5 The effect of the pH (2, 3, 4, 5, 6, 7, 9) for the production of

ALA by Rp in SSF. 105

4.6 The effect of the incubation period (1, 2, 3, 4, 5 days) for

the production of ALA by Rp in SSF. 107

4.7 The Plackett-Burman design matrix for the preliminary

screening of seven independent variables on for the

production of ALA by Rp in SSF. 113

4.8 Analysis of Variance (ANOVA) for the Plackett-Burman

design to determine the most significant factors on the

optimisation of the ALA by Rp in SSF. 114

4.9 Box-Behnken experimental design matrices for the

optimisation of the production of ALA by Rp in SSF. 117

4.10 ANOVA analysis for the optimisation of the ALA

production in SSF using Box-Behnken design. 118

4.11 Box-Behnken Design with experimental and predicted ALA

yield for the production of the ALA by Rp via SSF. 132

4.12 Regression Coefficients and ANOVA analysis for the

optimisation of the ALA by Rp in SSF. 135

4.13 ANOVA for the optimisation of the production of ALA

with the additional of supplementary substrates using Box-

Behnken design. 142

4.14 The fitted kinetic parameters and comparison between the

Logistic and the Gompertz model for the Rp Growth model

to produce ALA in the SSF experiment. 149

4.15 The fitted kinetic parameters and comparison between

Gompertz and Luedeking-Piret model along with different

case studies for the formation of the ALA model by Rp in

SSF. 157

xiv

4.16 The fitted kinetic parameters and comparison between

Luedeking-Piret model along with different case studies for

the consumption of the substrates model by Rp in SSF. 163

4.17 The computed kinetic parameter constants using the

Logistic model incorporated and validated with different

experimental data set from different studies to represent the

production of ALA by Rp.

166

xv

LIST OF FIGURES

FIGURE NO. TITLE PAGE

2.1 Integrated bioprocess systems engineering (BPSE)

methodology framework in combination of metabolic

approach and integrated process system engineering

approach from macroscopic scale (basic) to microscopic

scale (complex). 13

2.2 World Palm Oil Production from the year 2008 to year

2011. 15

2.3 Micrograph of gram-negative bacteria,

Rhodopseudomonas palustris (Rp) in budding rod shape

(a) before applying the safranin solution (b) after

applying the safranin solution. 19

2.4 Overview of physiology to support the growth of

Rhodopseudomonas palustris (Rp). 20

2.5 Overview on growth properties and useable substance in

Rhodopseudomonas palustris (Rp) and “+” means

available; “-” means unavailable and “+/-” means vary

based on condition. 21

2.6 Chemical structure of ALA in 2D (left) and 3D (right)

form. 22

2.7 Two alternative biosynthesis pathways to produce

Vitamin B12 (1) from D-Glucose (8) in producing the

intermediate ALA metabolite (2) via the (C4) Shemin

Pathway and the C5 pathway. 26

2.8 Formation of 5-aminolevulinic acid (ALA) from

xvi

succinyl-CoA and glycine by catalyst 5-aminolevulinic

acid synthase (ALAS).

27

2.9 Condensation process of ALA when mixed with

acetylacetone to yield ALA-pyrrole with free α-position. 29

2.10 ALA-pyrrole (E) compound shows the appearance of

pink colour when free α position react with

dimethylaminobenzaldehyde (DMAB). 29

2.11 Derivatisation process of ALA when reacted with

Fluorescamine to form Pyrrollinone. 30

2.12 Illustration of the microbial growth using particular

substrate as solid support medium via SSF. 33

2.13 Flowchart of the optimisation process for the production

of ALA by Rp in SSF. 41

2.14 Example of a Logistic model plot to describe the growth

of Rhizopus oryzae for the production of L-lactic acid. 51

2.15 Example of a Gompertz Model to describe the production

of Lipopeptide by Bacillus amyloliquefaciens XZ-173 in

SSF. 52

2.16 Example of a Luedeking-Piret Model to describe the

production of Lipopeptide by Bacillus amyloliquefaciens

XZ-173 in SSF. 55

3.1 Preparation of the dried and ground empty fruit bunch

(EFB) using the high-speed fibre grinder RT-20. 61

3.2 Starter Culture of Rp NRRL B-4276 for the production of

ALA. 63

3.3 The production of ALA by Rp in SSF. 64

3.4 The experimental design of the optimisation methodology

for the production of ALA by Rp in SSF. 65

3.5 Extraction of ALA from the fermented EFB samples

using the acetate buffer solution (pH 4.7). 74

3.6 The pretreatment filtration process of the analyses

solution prior to HPLC injection. 75

3.7 Colorimetric analysis of standard solution with different

xvii

concentration of standard ALA (0 mM, 2.5 mM, 7.5 mM,

10 mM, 12.5 mM, 20 mM, 40 mM) for standard

calibration curve (pink color).

76

3.8 Colorimetric analysis for determination of the total ALA

content (pink colour). 77

3.9 Calibration curve of standard ALA at wavelength

absorbance, 556 nm using colorimetric analysis. 77

3.10 Calibration curve of standard ALA using HPLC analysis 79

3.11 HPLC chromatogram of the standard 5-aminolevulinic

acid (ALA) solution using the fluorescence detector. 79

3.12 Calibration curve of the standard glycine using HPLC

analysis. 81

3.13 HPLC chromatogram of (a) standard amino acid, glycine

solution (b) a standard amino acid, glycine solution by

Bailey using UV-detector. 82

3.14 Calibration curve of standard Succinic Acid using HPLC

analysis. 83

3.15 HPLC chromatogram of Standard Succinic acid using

UV-detector. 84

3.16 A schematic design for the kinetic modelling of the

production of ALA by Rp via SSF. 85

3.17 The sequential steps for the modelling of Rp growth,

ALA formation and substrates consumption for the

production of ALA by Rp in SSF. 86

3.18 Summary of different case studies to represent the

Luedeking-Piret equation for the ALA formation model. 88

3.19 Summary of the different case studies to represent the

Luedeking-Piret equation for the substrate consumption

model. 92

4.1 The effect of the basal medium concentration (1, 2, 3, 4, 5

folds concentration) for the production of ALA in SSF by

Rp 97

4.2 The effect of the incubation temperature (20, 25, 30, 35,

xviii

45 °C) for the production of ALA in SSF by Rp. 99

4.3 The effect of the inoculum density (20, 30, 40, 50, 60%)

for the production of ALA in SSF by Rp. 101

4.4 The effect of the moisture content (40, 50, 60, 70, 80%)

for the production of ALA by Rp in SSF. 103

4.5 The effect of initial pH (2, 3, 4, 5, 6, 7, 9) for the

production of ALA by Rp in SSF. 105

4.6 The effect of the incubation period (1, 2, 3, 4, 5) for the

production of ALA by Rp in SSF. 108

4.7 Pareto chart standardised effects of seven variables using

screening Plackett-Burman design for the production of

ALA by Rp in SSF. 111

4.8 Normal Probability Plots of the standardised effect for for

the production of ALA by Rp in SSF using Placket-

Burman design. 112

4.9 Residuals plots presenting adequacy regression model for

the ALA production by Rp via SSF. 121

4.10 Response Surface plots representing the interacting effect

of two independent variables on ALA production by Rp

via SSF. 123

4.11 Contour plots representing the interacting effect of two

independent variables on ALA production by Rp via SSF 124

4.12 Contour plot (a) and response surface plot (b) for the

interaction effect of moisture content (%) and

concentration of glycine (mM). 126

4.13 Contour plot (a) and response surface plot (b) presenting

the interaction effects of temperature (°C) and

concentration of levulinic acid (LA) (mM) on the

production of ALA by Rp. in SSF. 129

4.14 The Response Optimisation plot with the optimum

combination variables to produce maximum ALA yield. 130

4.15 Contour and response surface plots presenting the

interaction effects of two different variables optimisation

xix

of the ALA formation via SSF. (a) Interaction effect of

succinic acid and glycine (b) Interaction effect of glycine

and levulinic acid (c) interaction effect of succinic acid

and levulinic acid.

136

4.16 Contour plot (a) and response surface plot (b) showing

the interactive effect of initial supplementary of succinic

acid and glycine while holding the total levulinic acid at

5mM for the production of ALA. 137

4.17 Contour plot (a) and response surface plot (b) showing

the interactive effect of initial supplementary of levulinic

acid and glycine while holding the total succinic acid at

5mM for the production of ALA. 140

4.18 Residuals plots presenting the adequacy of Box-Behnken

regression model adequacy for the production of ALA

with supplementary substrates in SSF. 143

4.19 The response optimisation plot with the optimum

combination of supplementary of substrates to produce

maximum ALA yield under optimised physiochemical

condition. 144

4.20 The kinetic profile describing the growth of Rp NRRL B-

4276 Growth in SSF under optimum condition. 147

4.21 Comparison between the Logistic model and Gompertz

model for the growth of Rp NRRL B-4276 in SSF under

optimised condition. G1 represents as the Logistic model

and G2 denotes as the Gompertz model. 151

4.22 The kinetic profile describing the growth of Rp NRRL B-

4276, the formation of ALA and the final pH of Empty

Fruit Bunch (EFB) in SSF under optimum condition 154

4.23 Comparison between Gompertz and different case studies

of Luedeking Piret Model for ALA formation in SSF

under optimized condition. P1 denotes Gompertz model

and P2 is Luedeking-Piret model: P2a, is the case study 1

where α≠0,β=0; P2b is the case study 2 where α=0 ,β≠0;

xx

P2c is the case study 3 where α≠0 ,β≠0. 158

4.24 The kinetic profile describing the growth of Rp NRRL B-

4276, the formation of ALA and the consumption of the

feed substrates (i.e. succinic acid and glycine) in SSF

under optimum condition. 160

4.25 Comparison between different case studies of Luedeking

Piret Model for the substrates consumption in SSF under

optimised condition. S1 denotes Luedeking-Piret model

and S1a is the case study 1 where dX/dt≠0, dP/dt=0; S1b

is the case study 2 where dX/dt=0 ,dP/dt≠0; S1c is the

case study 3 where dX/dt≠0, dP/dt ≠0. 164

4.26 The modelling of the Rp growth, ALA formation and

substrates consumption using the Logistic model

incorporated with Luedeking-Piret model for the

production of ALA by Rp in SSF. G1 denotes the

predicted data using Logistic model for Rp growth model,

P1c is the predicted data using Luedeking-Piret model

(case study 3) for ALA formation model and S1c is the is

the predicted data using Luedeking-Piret model (case

study 3) for the substrate consumption model. 167

xxi

LIST OF ABBREVIATIONS

Rp - Rhodopseudomonas palustris

ALA - 5-Aminolevulinic Acid

BPSE - Bioprocess systems engineering

EFB - Empty Fruit Bunch

Mt - Million tonnes

SmF - Submerged Fermentation

LA - Levulinic acid

Gly - Glycine

Succ - Succinic Acid

RSM - Response Surface Methodology

HPLC - High-Performance Liquid Chromatography

MSW - Municipal Solid Waste

EM - Effective Microorganism

ATP - Adenosine triphosphate

ALAS - 5-aminolevulinic acid Synthase

ALAD - 5-aminolevulinic acid dehydratase

DMAB - dimethylaminobenzaldehyde

E.coli - Escherichia coli

PDB - Precursor-directed biosynthesis technique

ODE - Ordinary Differential Equation

PDE - Partial Differential Equation

GM - Glutamate medium

ANOVA - Analysis of Variance

xxii

LIST OF SYMBOLS

f(x) - Function of the independent variables

µm - Maximum specific growth rate (hr-1

)

X - Rp biomass concentration (CFU/g)

t - Fermentation duration (hr)

Xmax - Maximum concentration of Rp (CFU/g)

X - Concentration of biomass (CFU/g)

P - Concentration of ALA (mg/kg)

S - Concentration of feed substrates (mg/l)

α - Growth-associated coefficient

β - Non-growth associated coefficient

YX/S - Yield coefficient of biomass growth on substrates used

YP/S - Yield coefficient of product formed on substrates used

Rm - Specific product formation rate

Pmax - Maximum concentration of product (ALA)

R2 - The determination of coefficient

h - hours

g/L - Gram per Litre

mg/kg - Milligram per kilogram

µM - Molar concentration

xxiii

LIST OF APPENDICES

APPENDICES TITLE PAGE

A The kinetic profile of the Rp NRRL B-4276 growth

(Colony Forming unit, CFU) under optimised condition

in SSF 193

B The Matlab script for the estimation of kinetic

parameters with respect to the observed experimental

data using ODE45 solver and lsqcurvefit tool for Rp

growth model (for example: Logistic model) 194

C The Matlab script for the evaluation of how fitted of the

proposed model to the experimental data for the growth

of Rp model using the determination of coefficient, R2

(For example: Logistic Model) 195

D Comparison between Logistic and Gompertz model for

the growth of Rp NRRL B-4276 growth in SSF under

optimised condition 196

E The kinetic profile of the formation of 5-aminolevulinic

acid (ALA) (mg/kg EFB) by Rp in SSF 197

F The change in the final pH value of the EFB 198

G The Matlab script for the estimation of kinetic

parameters with respect to the observed experimental

data using ODE45 solver and lsqcurvefit tool for the

ALA formation model (for example: Luedeking-Piret

model, P2c, Case studies 3) 199

H The Matlab script for the evaluation of how fitted of the

proposed model to the experimental data for the

xxiv

formation of ALA model using the determination of

coefficient, R2 (For example: Luedeking-Piret Model,

P2c, Case study 3)

200

I Comparison between Gompertz and different case

studies of Luedeking Piret Model for the formation of

ALA in SSF under optimised condition 201

J The kinetic profile of the concentration of the

supplementary precursor substrate, succinic acid (mM)

for the production of ALA via SSF by Rp 202

K The kinetic profile of the concentration of

supplementary precursor substrate, glycine for the

production of ALA via SSF by Rp 203

L The kinetic profile of the total feed substrates (succinic

acid (g/L) and glycine (g/L)) for the production of ALA

via SSF by Rp 204

M The Matlab script for the estimation of kinetic

parameters with respect to the observed experimental

data using ODE45 solver and lsqcurvefit tool for the

substrates consumption model (for example: Luedeking-

Piret model, S1c, Case study 3) 205

N The Matlab script for the evaluation of how fitted of the

proposed model to the experimental data for the

consumption of the substrate model using the

determination of coefficient, R2 (For example:

Luedeking-Piret Model, S1c, Case study 3) 206

O Comparison of different case studies of Luedeking Piret

Model for the consumption of the substrates model in

SSF under optimised condition 207

P Validation of the proposed model using different set of

experimental data under optimised condition for the

production of ALA by Rp via SSF (For example: The Rp

growth model) 208

Q Validation of the proposed model using different set of

xxv

experimental data under optimised condition for the

production of ALA by Rp via SSF (For example: The

formation of ALA model)

209

R The validation of the Logistic model incorporated with

Luedeking-Piret model using different experimental data

sets under optimised condition for the production of

ALA by Rp via SSF (For example: The consumption of

the substrates model) 210

S Paper 1 (Abstract) Book Chapter 211

T Conference Proceeding 1 212

CHAPTER I

INTRODUCTION

1.1 Research Background

In the 21st century, agricultural technology has rapidly developed in order to

promote the agricultural activity and satisfy the worldwide food demand for growing

population in both developing and developed countries. Even though traditional

chemical fertiliser has been long applied to increase the crop yield and shorten the

ripening time of the crop, it could still lead to serious environmental problems.

Beneficial microbial inoculant has raised the attention of the researchers in order to

produce high effective and efficiency of compost or fertiliser as an alternative to

chemical fertilisers [1]. The application of beneficial microbial inoculants to produce

biofertiliser or compost not only helps to stimulate the decomposition of organic

waste and residues, detoxify the pathogen, suppress plant diseases, enhance the

nutrient uptake of plant but also produce bioactive substrates to promote the plant

growth, crop yield and maintain the sustainability of environmental [2].

Among the well-known beneficial microbial inoculants, Rhodopseudomonas

palustris (Rp) is one of the predominant beneficial microorganisms that shows high

potential to increase the plant uptake of nutrients, stimulate the plant growth and

2

directly enhancing the crop growth and crop productivity [3,4]. Many studies have

reported that the inoculation of Rp as biofertiliser or soil inoculants could promote

the high yield of fruits and crops, enhance the acids tolerance in plant, able to fix the

atmospheric nitrogen as their nitrogen source, directly reduce the use of chemical

fertilizers and lead to more eco-friendly and sustainable agricultural practices [5].

Additionally, Rp is also well-known to produce potentially useful bioactive

substrates like 5-aminolevulinic acid (ALA). ALA gained much attention as an

effective, harmless, natural herbicide and insecticide under normal or stress condition,

it has also been used to regulate, promoting the photosynthetic rate of plant growth

for high crop yields [6-8].

For the purpose of these advantages given by microbial inoculants like Rp, a

number of studies and research works have been carried out to study the effects of

the application of microbial inoculants, plant growth and crops yield extensively [5].

In the market today, a variety of microbial biofertilisers are available that claim to

stimulate plant growth and crop productivity. Many products are still lack of the

scientific point of view due to several reasons:

(i) often the microbial inoculant involves mixed culture that is not

specified in detail

(ii) the difficulties to reproduce their beneficial effect consistently

(iii) The complexity of the interactions effects among the microbial

inoculants in the bio-system, thus, difficult to evaluate the product and

its underlying behaviour or process in the compost [1,2].

Therefore, there is a need to carefully coordinate the standard methodology

and devise a suitable technique to identify and better understanding the roles of each

microbial and its interaction in the potential environmental, this will indirectly help

in producing highly effective and efficient biofertiliser.

3

Bioprocess systems engineering (BPSE) has long been developed and

recognised as a high promising methodology to better understanding the complexity

of the biological process and the whole bio-system for over the past 20 years. BPSE

offers a strategy to the living microorganism to enhance the cell growth, maximise

the productivity of the target product and help in the production yield, while

minimising the overall operation costs and reduce the environmental impact [9].

Additionally, BPSE has also implemented mathematical modelling as an essential

tool to facilitate the fundamental understanding and provide insights into how the

various behaviour within the complex bio-system from the engineering point of view

[10]. Limited studies have investigated the production of ALA in the complex system

like composting using the systematic methodology of BPSE. Hence, leveraging on

the concept of BPSE one can better understand the underlying behaviour of ALA

production by Rp in a complex system using series of the systematic methodology of

BPSE.

1.2 Problem Statement

Malaysia has long been recognised as the world second top oil palm producer

with 5.39 million hectares of oil palm planted area and a total of 423 palm oil mills

operators [11]. Due to the large scale of oil palm production, the oil palm mills

process have generated approximately 53 million tonnes (MT) of residues waste

every year. Empty fruit bunch (EFB) is the main residues from the fresh fruit bunch

after the palm oil extraction process and it is contributing an average of 23.8 MT

among the generated solid residues waste from oil palm industries [11]. Improper

discharged of the large amount of EFB to the environmental might create negative

impact to the environment [11,12]. Composting has been proposed as one of the

preferable and eco-friendly options to convert EFB into an inexpensive organic

fertiliser for solid waste recycling, replacement of chemical fertiliser use, reducing

EFB waste’s volume and helps to enhance the palm oil productivity in the plantation

to achieve sustainable economic growth [13].

4

The promising characteristic of 5-aminolevulinic acid (ALA) to regulate and

enhance the plant growth effectively has created the demand for ALA in agricultural

practice [14]. Cultivation of photosynthetic bacteria like Rhodopseudomonas

palustris (Rp) in submerged fermentation (SmF) are the widely studied fermentation

process for ALA production but involved considerable high operation cost, there is a

need to suggest a cheaper and economical approach to producing the desirable

amount of ALA especially for the agricultural application. Solid state fermentation

(SSF) is one of the microbial fermentation techniques which involve the microbial

cultivation process on near absence of a free water solid medium. It involves low

capital and operating cost expenses are the attractive reason to be chosen for ALA

production [15].

The replacement of the solid medium by industrial residues like EFB not only

provides a suggestion to handle EFB from oil palm industrial but also increases the

economic value for EFB and making the ALA production process more cost

effective. The primary concern of this research work is to better understand the roles

of Rp and its respective interaction in the natural environment and indirectly help to

produce highly effective and efficient compost. EFB can be considered as fibrous

material that consists of less chemicals or mineral additives if compared to others oil

palm residues like palm oil mill effluents [16]. The suggestion of ALA production by

Rp on the EFB via SSF process could serve as one well-controlled system that

mimics the composting process to further investigate the overall performance of Rp

to produce ALA in compost.

There is a knowledge gap for the production of ALA by Rp in the solid state

habitat with regard to the key factors promoting the ALA production. Inoculation of

a beneficial microorganism to compost is expected to be beneficial by providing the

suitable and optimum condition for the optimum metabolism of the microbes

including the available water content of the environment, pH of the medium,

temperature of the surrounding environment and available substrates uptake [1]. The

environmental condition and supplementary substrate requirements for Rp in EFB to

produce ALA have yet to be demonstrated.

5

The kinetic model commonly aims to describe the overall performance of a

complex biological system, the interaction behaviour between the microbial and its

respective metabolites quantitatively [17]. Kinetic models have been studied

extensively in submerged fermentation (SmF) to produce ALA, relatively there is

still a lack of kinetic studies to better understand the ALA production via SSF [8]. It

is also a challenge to find the most appropriate model to give a better description of

the observation in the experimental results. Therefore, it is more reasonable to

compare different models to evaluate the well-fitted model to describe the overall

performance of the particular process.

1.3 Objectives of the Study

This study aims to study the production of 5-aminolevulinic acid (ALA) by

Rhodopseudomonas palustris (Rp) via solid state fermentation (SSF) using empty

fruit bunch (EFB) as the solid support medium. The sub-objectives of this study

include:

a) To optimise the physiochemical parameters and the concentration of

supplementary substrate for the production of 5-aminolevulinic acid (ALA)

by Rhodopseudomonas palustris (Rp) in solid state fermentation (SSF)

b) To develop the most plausible kinetic model to best describe the kinetic

behaviours of Rp to produce the ALA in SSF process under the resulted

optimised condition.

6

1.4 Scopes of the Study

The scopes of the study include:

a) To investigate the initial estimation of the parameter value ranges for the

production of 5-aminolevulinic acid (ALA) in the solid state fermentation

(SSF) using empty fruit bunch (EFB) as the solid support medium by

Rhodopseudomonas palustris (Rp) using the classical one-factor-at-a-time

method.

b) To determine the initial value range of the physiochemical parameters

including the concentration of the impregnating nutrient medium (1-5 folds

concentration), incubation temperature (25-45ºC), inoculum density of Rp

(20-60 %(v/w)), initial moisture content level (40-80%), initial pH (3-9), and

the incubation period (1-5 days) to produce high yield of ALA by Rp in SSF

using one-factor-at-a-time method.

c) To characterise the most significant physiochemical parameters among the

selected parameters and further optimise the resulted significant

physiochemical parameters (i.e.: moisture content, temperature, glycine,

levulinic acid) to achieve a high yield of ALA using full-factorial method,

Plackett-Burman design and Response Surface Methodology (RSM), Box-

Behnken design.

d) To determine the optimum concentration of the supplementary substrates

including glycine, succinic acid and levulinic acid to obtain a high yield of

ALA via SSF under the resulted optimised fermentation condition.

e) To investigate the kinetic profiles of ALA synthesis, substrate uptake and Rp

growth rate versus a time course under the optimised condition and

characterise the total amount of ALA, substrates and Rp population

quantitatively by using the colorimetric method, high-performance liquid

chromatography (HPLC) and cell-plate counting method, respectively.

7

f) To select the most compatible kinetic model and estimate the kinetic

parameters constant in the model to best describe the kinetic behaviour for

the growth of Rp, substrate consumption and ALA formation for the

production of ALA by Rp in SSF process.

g) To compare the experimental data from different studies to validate the

robustness of the proposed model for the production of ALA by Rp in SSF.

1.5 Significant of the study

This research work contributes to improve the fundamental knowledge and

understanding about the 5-aminolevulinic acid (ALA) production by

Rhodopseudomonas palustris (Rp) via solid state fermentation (SSF) using empty

fruit bunch (EFB) as solid support medium from the theoretical and practical

perspectives. Comprehensive bioprocess, optimisation and modelling tools are highly

recommended to make the process economically viable.

Firstly, there is a limited study to produce ALA by Rp via SSF using EFB as

the solid support medium. Though there are studies considering the production of

ALA via submerged fermentation (SmF), over the last few decades, there is an

increasing trend to produce the value-added metabolites via SSF due to its

advantages such as simpler process, less energy consumption, low capital cost using

industrial agro-waste and less downstream processing. Based on the optimised result,

the production of ALA by Rp in EFB can be applied for industrial large-scale

production.

8

Secondly, the kinetic models are developed with important engineering

characteristic to provide fundamental knowledge of the complex biological system.

There is limited report conducted as a benchmark to further investigate the

underlying kinetic performance for the production of ALA by Rp via SSF process

using the engineering approaches. With increasing datasets of different metabolites

production and more advanced mathematical description, the developed model could

serve as a basic model to predict the performance of ALA production by Rp when

there are changes in the rate and composition of the substrates.

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