DEPARTMENT FOR ENVIRONMENT FOOD & RURAL AFFAIRSrandd.defra.gov.uk/Document.aspx?Document=14129_SOP-FA0158.pdf · sop fa0158, version 2.0 page 1 of 32 department for environment food

SOP FA0158, Version 2.0

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DEPARTMENT FOR ENVIRONMENT FOOD & RURAL AFFAIRS

(DEFRA)

STANDARD OPERATING PROCEDURE (SOP)

Version 2.0, September, 2016

STANDARD OPERATING PROCEDURE FOR DEVELOPMENT OF A TWO-

STEP METHODOLOGY TO DETERMINE VEGETABLE OIL SPECIES IN

VEGETABLE OIL MIXTURES, PASTRY AND CONFECTIONERY

PRODUCTS

Prepared by Dr Tassos Koidis, Queen’s University Belfast, Date 09/09/16

Approved by _________________________ Date _______________


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CONTENTS

1. HISTORY / BACKGROUND ...................................................................................................... 3

1.1 BACKGROUND ............................................................................................................................. 3

2. PURPOSE ...................................................................................................................................... 3

3. SCOPE ........................................................................................................................................... 3

4. DEFINITIONS AND ABBREVIATIONS .................................................................................. 3

5. PRINCIPLE OF THE METHOD................................................................................................ 4

6. MATERIALS AND EQUIPMENT ............................................................................................. 5

6.1 CHEMICALS ................................................................................................................................. 5 6.2 WATER ....................................................................................................................................... 5 6.3 SOLUTIONS, STANDARDS AND REFERENCE MATERIALS ............................................................... 6 6.4 COMMERCIAL KITS ...................................................................................................................... 6 6.5 PLASTICWARE ............................................................................................................................. 6 6.6 GLASSWARE ................................................................................................................................ 6 6.7 EQUIPMENT ................................................................................................................................. 6

7. PROCEDURES ............................................................................................................................. 7

7.1 FTIR SPECTRA ACQUISITION ON OIL SAMPLES (PURE OR EXTRACTED FROM FOOD PRODUCTS) ... 7 7.2 PASTRY PRODUCTS- OIL EXTRACTION FROM BISCUITS .............................................................. 12 7.3 CONFECTIONERY PRODUCTS- OIL EXTRACTION FROM CHOCOLATE .......................................... 12 7.4 ANALYSIS OF FATTY ACIDS ....................................................................................................... 13 7.5 QUALITY ASSURANCE ............................................................................................................... 15

8. CALCULATIONS AND DATA ANALYSIS ........................................................................... 15

8.1 SCREENING STEP BASED ON SPECTROSCOPIC DATA (FTIR) ....................................................... 15 8.2 CONFIRMATION STEP BASED ON CHROMATOGRAPHIC DATA (FATTY ACID BY GC) .................... 28

9. RELATED PROCEDURES ....................................................................................................... 31

10. ESSENTIAL REFERENCES .................................................................................................... 31

11. APPENDICES ............................................................................................................................. 31


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1. HISTORY / BACKGROUND

1.1 Background

It is common practice for food manufacturers to use refined vegetable oil mixtures as ingredients in

confectionery, pastry, bakery and other food products. These mixtures are mostly composed of

refined palm oil, sunflower oil and to lesser extents rapeseed, corn, coconut, cottonseed oils. Palm

oil, the largest volume oil imported into UK, is used in high amounts. Until very recently there was

no requirement for manufacturers to state the composition of the mixture and it was labelled under

the generic term “vegetable oil”. With the recent EC1169/2011 regulation regarding vegetable oil

labelling, the composition of vegetable oil must be declared in the label (European Commission,

2011). Food manufacturers must comply with this new requirement, although normally this is not a

challenge for the industry as information on composition should be available via the product

specification provided from their oil suppliers. It presents, however, a challenge for the legislation

and enforcing authorities such as DEFRA amongst others, to monitor compliance of EU Legislation

and correctly labelled foodstuffs.

2. PURPOSE

The purpose of this SOP is to provide with a methodology that will allow identifying the oils species

present in a refined vegetable oil blend as well as in a pastry/biscuit product. Additionally the

presence or absence of palm oil species in confectionery products can also be detected following this

SOP.

3. SCOPE

The methodologies described in this SOP are suitable for the qualitative identification of vegetable

oil species in an oil blend or in oil extracted from a pastry and confectionery product. The

methodologies are validated to concentration of at least 15% of one oil in another oil which is the

most common case in oil blends intended for processed foods. These methodologies are limited to

the oil species used for the calibration models (palm oil and its derivatives, sunflower oil and

rapeseed oil). They are not suitable for the identification of oil species in oil blends containing three

or more different oils.

4. DEFINITIONS AND ABBREVIATIONS

FTIR: Fourier Transform Infrared (spectroscopy)

FA: Fatty acid

FAME: Fatty acid methyl ester

GC: Gas chromatography


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GC-FID: Gas Chromatography-Flame Ionisation Detector

PCA: Principal Component Analysis

SIMCA: Soft Independent Modelling of Class Analogy

PLS-DA: Partial Least Square-Discriminant Analysis

PLS-R: Partial Least Square-Regression

5. PRINCIPLE OF THE METHOD

5.1 Determination of oil species in oil admixtures

The methodology employed is a staged procedure that consists of a combination of FTIR

spectroscopy that is used to screen and classify the oils and the well adopted fatty acid methyl esters

analysis using gas chromatography to confirm the composition of the oils when required.

These two techniques, when performed serially on the basis of the developed decision making

system, exploit the small differences of the chemical composition between different oil species in

different type of oil blends to classify the unknown sample in one of the 6 or 12 oil classes studied.

In that way, both untargeted analysis (spectroscopic screening) and targeted approaches (fatty acid

quantification by gas chromatography) are applied to increase result’s certainty. The system is

designed to target the following oil classes:

Legacy 6 classes’ model: PKOC- palm kernel oil, coconut oil, P- palm oil including olein and

stearin, RS- rapeseed and sunflower oil and their admixtures, PPKOC- admixtures of P and

PKOC class, RSPKOC- admixtures of RS and PKOC class, and RSP- admixtures of RS and

P

High resolution 12 classes’ model: PKO- palm kernel oil, RO- rapeseed oil, SO- sunflower

oil, P- palm oil, palm olein and palm stearin, ROSO- rapeseed and sunflower oil admixture,

ROPKO- rapeseed and palm kernel oil admixture, SOPKO- sunflower and palm kernel oil

admixture, ROPO- rapeseed and palm oil admixture, SOPO- sunflower and palm oil

admixture, PPKO- palm oil and palm kernel oil admixture, PCCO- palm oil and coconut oil

admixture and CCO- coconut oil.

5.2 Determination of oil species in pastry products (biscuits)


spectroscopy that is used to screen and classify the oils extracted from biscuit products and the fatty

acid methyl esters analysis using gas chromatography to confirm the composition of the biscuit

extracted oils when required.

Both untargeted analysis (FTIR spectroscopic screening) and targeted approaches (fatty acid by GC)

are applied to the oil extracted from a biscuit product. The system is designed to target the following

oil classes:


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PO class model: palm oil

PORO class model: palm oil and rapeseed oil admixtures

RO class model: rapeseed oil

5.3 Detection of presence of palm oil species in a confectionery product


spectroscopy that is used to screen and detect the presence of palm oil species in the oils extracted

from confectionery products and the fatty acid methyl esters analysis using gas chromatography to

confirm the presence or absence of palm oil species when required.

Both untargeted analysis (FTIR spectroscopic screening) and targeted approaches (fatty acid

by GC) are applied to the oil extracted from a confectionery product. The system is designed to

target the following oil classes:

P class model: palm oil species

Non-P class model: absence of palm oil species (most probably presence of cocoa butter)

6. MATERIALS AND EQUIPMENT

6.1 Chemicals

For spectroscopic measurements:

Ethanol, analytical grade

For analysis of fatty acids:

Methanol, HPLC grade.

Potassium hydroxide, AR grade, (≥85% KOH basis, pellets, white), Sigma P1767

Sodium sulphate anhydrous, (ACS reagent, ≥99.0%, anhydrous, granular), Sigma

239313.

Hexane, HPLC grade

All chemicals were purchased from Sigma-Aldrich (http://www.sigmaaldrich.com/united-

kingdom.html). No special storage requirements were essential or particular hazards identified.

6.2 Water

No water is used.

http://www.sigmaaldrich.com/united-kingdom.html

http://www.sigmaaldrich.com/united-kingdom.html


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6.3 Solutions, standards and reference materials

Fatty acid methyl ester standard commercial mixture. Provided by Sigma-Aldrich,

Product code 47885-U

Internal standard: Methyl tridecanoate. Provided by Sigma-Aldrich. Product code

91558-5ml.

6.4 Commercial kits

Not applicable.

6.5 Plasticware

Pipettes tips: 1mL, 5mL, typical (polypropylene, any supplier such as Thermo Fisher

Scientific, Dublin, Ireland)

Safety pipette filler, typical (polypropylene, Thermo Fisher Scientific, Dublin,

Ireland)

6.6 Glassware

4 mL glass vials to store the oils after extraction. An example is the vial glass sample

with attached black poly-seal cone caps 4mL 15mm x 48mm clear supplied by Fisher

Scientific Ltd. (Product code 11660112)

Measuring cylinder, glass, 100ml

Measuring cylinder, glass, 1000ml

Reagent bottle, glass, 1000ml

Reagent bottle, glass, 100ml

Graduated pipette, glass, 1ml

GC vials with screw cap and septum, 2ml

Pasteur pipettes, glass, 250mm with filler

No special cleaning produce is applied.

6.7 Equipment

For spectroscopic measurements:


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A standard Fourier Transform Infrared spectroscopic equipment: An example is the Nicolet iS5

Thermo Scientific (Thermo Fisher Scientific, Dublin, Ireland). ATR iD5 accessory-diamond.

Detector: DTGS KBr. Beamsplitter: KBr. Product code: not available

A typical dry block heater capable of reaching higher than 50°C. An example is the model: 25H

heated and stirring ambient +5°C to 150°C temperature range purchased from Thermo Fisher

Scientific (Dublin, Ireland, product code: 11767519).

Automatic pipettes ‘eppendorf’: 1mL, 5mL (such as the Eppendorf Research® plus)

For chromatographic analysis of fatty acids:

Oven set at 100°C.

Desiccator.

A typical Gas Chromatography- coupled with Flame Ionisation Detector: an example is the Varian

CP3800 Gas chromatograph supplied by JVA Analytical, Dublin. Alternatively, gas chromatography

coupled with mass spectrometer can also be used.

GC analytical column CP-88-SIL for FAME, 100m x 0.25mm id, 0.2µm film thickness. Supplied by

Agilent Technologies, Product number CP7489.

6.8 Software

A standard multivariate analysis software is required. There are many such products available to

users. An example used here is SIMCA v 14 by Umetrics (Malmö, Sweden).

MATLAB version 8 or higher (Mathworks, Natick, MA, USA) is required to run the most advanced

data analysis described in the SOP.

Any acquisition software that is bundled with the FTIR instrument. Since Thermo Fisher FTIR is

used for the acquisition of spectra, OMNIC software is used here for data acquisition and handling.

7. PROCEDURES

All oil samples should be stored in amber containers protected from the light at 4°C/-20°C and used

within 6 months to minimise variation introduced from potential autoxidation of the oils.

7.1 FTIR spectra acquisition on oil samples (pure or extracted from food products)

Melt the oil sample in an air oven at 50°C until clear if necessary (only for solid oils at room

temperature).

Take aliquots of 1 mL into small glass vials using a pipette.

Place the vials in a block heater at 50°C prior measurement in FTIR.


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Clean the surface of the ATR diamond of the FTIR twice with ethanol or isopropanol and a

soft tissue and let it dry.

Take the sample from the block heater and gently shake it manually.

Place 2-3 drops of oil on the surface of the ATR diamond using a pipette. Check there are no

air bubbles.

Record the FTIR spectra at room temperature using the OMNIC software, the acquisition

software bundled with the Thermo FTIR instrument. Note that other manufacturers will have

other softwares with the same functionality.

Open the OMNIC software:

- Click ‘Collect’ and then ‘Experiment setup’. Establish the operational conditions of the FTIR prior

to analyse the samples (Table 1):

Primary parameters:

1. Spectra resolution: 4.0

2. Number of sample scans: 32

3. Spectral range: 600-4000 cm-1

4. Zero filling: 2 levels - If not possible, leave ‘None’ or the ‘Default’

Secondary parameters:

5. Apodization function: N-B Strong

If you don’t have N-B Strong, please select the equivalent ‘Triangular’ or ‘Triangular

squared factor of 2-4’

If you don’t have the ‘Triangular’, please select ‘None’ or leave the ‘Default’ option of

your instrument

6. Phase correction: Mertz

If it cannot be defined, please select ‘None’ if possible. If not, leave the ‘Default’

option of your instrument

7. Format of the spectra: *.SPA

If not, please save the files as type CSV Text (*.csv)

If you don’t have .csv, please save the files as *.grams (*.SPC)

If you don’t have *.grams, please save the files in the suggested format by your software

Table 1. Desirable acquisition parameters and other alternative parameters

DESIRABLE

1st

ALTERNATIVE

2nd

ALTERNATIVE

3rd

ALTERNATIVE

Primary

parameters

Spectra resolution 4.0

Number of sample 32


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scans

Spectral range (cm-1) 600-4000

Zero filling 2 levels None Default

Secondary

parameters

Apodization function

N-B Strong

Triangular or

Triangular

squared factor of

2 – 4

Strong or Happ-

Genzel

Default option on

your instrument

Phase correction Mertz Default

Format of spectra *.SPA

CSV Text

(*.CSV) *.grams (*.SPC) Default format


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Click ‘Collect’ and then ‘Collect sample’. Enter the spectrum title and click ‘OK’.


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Confirm the collection of the background spectra clicking ‘OK’. And click ‘OK’ once again

to confirm the collection of the sample spectrum.

Click ‘Yes’ to confirm that data collection has stopped and you want to add the collected

spectrum to a particular window (e.g. window 1).

Click ‘File’ – ‘Save as’ and introduce the name of the file (e.g. the same name given before to

the spectrum tittle)


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Clean the oil from the ATR with ethanol or isopropanol after each measurement.

At least three spectra should be taken from each sample.

Replicates should be averaged.

7.2 Pastry products- Oil extraction from biscuits

Grind the biscuits (approx. 50 g) finely with a grinder.

Mix the ground biscuit powder with n-hexane (1:2) in 50 mL centrifuge tubes (13 ~ 15

g/biscuit powder with 30 mL n-hexane in each tube) and place them on a roller mixer. Allow

the tubes in the roller mixer for 1 hour (33 rpm with 16 mm amplitude) for dissolving the oils

in the solvent.

Centrifuge the tubes containing the biscuit powder and solvent at 3000×g for 10 minutes to

separate the powder from the solvent.

Transfer the upper layer containing the oil dissolved in the solvent into a 50 mL round-

bottomed flask for the evaporation of the solvent using a rotary evaporator.

Place the flask in a rotary evaporator at 60°C and 160 rpm for 15 minutes.

After the evaporation of the solvent, transfer the oil into a small vial and keep the vial at -

20°C until further analysis.

7.3 Confectionery products- Oil extraction from chocolate

Manually mill the confectionery product into powder/fine particles using a knife or a wooden

stick. If the confectionery product does not have chocolate, an electric grinder could be used

instead.

Mix 10 g of sample with 30 mL of hexane in a 50 mL centrifuge tube.

Place the tube in a tube mixer at 2500 rpm during 2 minutes. Alternatively, it can be mixed

manually in a vortex.

Place the tube in a rotary mixer (33 rpm) during 1 hour letting the fat be dissolved in the

solvent.

Centrifuge the tube at 3000 rpm during 10 minutes until total separation of phases.

Transfer the upper layer containing the fat dissolved in hexane to a round bottomed flask.

Add 30 mL of hexane to the remaining bottom layer for a second extraction.


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Repeat the steps followed for the first extraction.

Transfer the second upper layer containing the remaining fat dissolved in hexane to the round

bottom flask and mix with the first extraction.

Evaporate the solvent using a rotary evaporator at 50°C during 15 minutes (160 rpm).

Transfer the fat into a small glass vial.

Repeat the extraction procedure as many times as needed in order to obtain the required

amount of oil sample (approx. 3 g).

Inject nitrogen into the headspace to prevent oxidation and stored the extracted oils at -20°C

until analysis.

7.4 Analysis of fatty acids

Fatty acid methyl esters (FAMEs) were prepared according to BS684-2.34:2001 part 5. Briefly, oil

blends are heated to 60oC to ensure complete melting of the solid fat component before being

thoroughly mixed prior to sampling. Subsamples (300 mg) are taken in duplicate and dissolved in 10

mL of hexane. An aliquot of the fatty acid methyl esters in hexane should be transferred to a vial

prior to analysis by gas chromatography (GC). Individual fatty acid methyl esters were detected by

flame ionisation detection (FID), identified by comparison with external fatty acid methyl ester

standards and quantified by the use of an internal standard. For detailed calculations go to the

procedure 8.2.2.

Methylation of fatty acids

7.4.1.1 Preparation of reagents

Sodium sulphate anhydrous:

Weigh approximately 50 ±0.01 g of sodium sulphate into a clean dry silica basin, place in an oven

for 2 hours ±10.0 minutes. Remove from the oven and cool to ambient temperature in a desiccator

before use.

Anhydrous-methanol:

Using a clean dry measuring cylinder measure out 1000 mL of methanol and transfer to a clean dry

reagent bottle. The reagent is stable for three months.

Methanolic Potassium hydroxide, 2N solution:

Weigh out 11.2 ±0.01 g of potassium hydroxide and transfer to a reagent bottle; using a clean dry

measuring cylinder. Add 100 ml of anhydrous methanol reagent and dissolve. The reagent is stable

for one month.

7.4.1.2 Preparation of fatty acid methyl esters (FAMEs)

Allow the samples to reach ambient temperature before use.


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Using a clean, dry graduated pipette and safety pipette filler, add 0.5 mL methanolic

potassium hydroxide reagent.

Cap the vial and thoroughly mix the contents for 30 seconds.

Allow the mixture to stand undisturbed until two clear layers are formed.

Using a clean dry disposable Pasteur pipette, transfer approximately 2 mL of the upper layer

to a clean dry labelled GC vial.

Close the GC vial with a cap and septum, store at –20°C in a spark-proof freezer, and analyse

within one week.

Chromatographic analysis of FAMEs

Heat the oil blends to 60oC to ensure complete melting of the solid fat component before

being thoroughly mixing prior to sampling.

Take subsamples (300 mg) in duplicate and dissolve them in 10 mL of hexane.

Transfer an aliquot of the fatty acid methyl esters in hexane to a vial prior to analysis by gas

chromatography.

Place the vial in the autosample of the GC-FID.

Adjust the GC-FID operating conditions as follow:

Injector

Injector temperature 225°C

Injection volume 2.0 µL

Split ratio 50:1

Carrier gas

Carrier gas flow rate 1.0 mL/min (constant flow).

Carrier gas helium.

Detector

Detector Flame ionisation detector.

Detector temperature 225°C.

Range 12

Column oven

Initial temperature: 70°C

8.0°C/min to 110°C, hold for 0.0 minutes.

5.0°C /min to 170°C, hold for 10.0 minutes.


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7.5 Quality Assurance

Sample preparation:

The oils to be tested were preheated at 50°C before the spectroscopic measurements.

Temperature check on the heat blocker has to be performed to ensure that oils are not over or

under heated which will introduce variation in the measurements.

The sample preparation procedure for fatty acid analysis is based on a BS method.

Storage of oils samples: All oil samples have to be stored individually in glass vials in the

dark with a headspace of <5% to avoid auto-oxidation and photo-oxidation.

Spectroscopic analysis:

FTIR spectra are acquired in triplicate.

Instruments should be calibrated before the measurements.

Equipment must be maintained according to manufacturer’s guidelines.

The spectral acquisition itself should not introduce any variation in the measurements if done

in a well maintained and calibrated spectrometer.

Spectra should be recorded by trained personnel.

Fatty acid analysis:

Fatty acid analysis with gas chromatography of fatty acids methyl esters (FAMEs) is performed

according to the official British Standards method (BS EN ISO 5509:2001; BS 684-2.34:2001)

Blanks are included within each batch of samples to establish base line stability and instrument

readiness. External standards are used to determine fatty acid retention times and individual fatty

acid response factors but not for instrument calibration. An internal standard (methyl tridecanoate) is

added to each sample prior to preparation and determination of the fatty acid methyl esters. All

analyses should be carried out in duplicate.

8. CALCULATIONS AND DATA ANALYSIS

8.1 Screening step based on spectroscopic data (FTIR)

Spectral data handling: Introduction of raw spectral data (.spa files) into an Excel file

Open TQ Analyst 8.


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Under tab ‘Standards’ click ‘Open Standard…’ to upload all the spectra into the program.

Choose ‘Spectra/Groups (*.SPA, *.SPG)’ in ‘Files of type’ in the ‘Open’ window. Select all

the FTIR spectra and press ‘Open’. All the spectra will appear in the Standard Table. Click on

‘Show spectrum file names’ and ‘Show spectrum titles’ (optional) to show that information

from the spectra in the Standard Table.

To save the spectral data in a .csv, go to ‘File’, and then click ‘Standards to text file’. A ‘Save

as’ window will appear. Choose the destination folder and the file name. Save as type ‘Text

(*.csv)’ and click ‘Save’.

Open Microsoft Excel. Go to ‘Data’ tab and click on ‘Get external data from text’. Find the

saved .csv file with the spectral data and click ‘Open’. Select all data and paste them as

transposed in a new sheet (sheet 2), so that the variables (wavenumbers) will be in columns

and the samples will be in rows.

Click ‘File’ and ‘Save as’ to save the final dataset. Choose the destination folder and the file

name. Save as type ‘Excel Workbook (*.xlsx)’ and press ‘Save’.

The spectral data need to be in an excel file in order to predict the oil species in the screening step.

The introduction of spectral data into an excel file can be done using different softwares (not

necessary using the TQ Analyst as described above). Every user can use their own way for having

the spectral data into an excel file.

The Excel DataSheet containing the FTIR spectral data of the unknown oil samples should be similar

to the one below:

Initial determination of oil species in an unknown oil blend using the 6 classes’ model (Model B)

Model B is a calibration model built with PLS-DA using MATLAB. The model is able to predict the

identity of unknown oil samples assigning the unknown samples to one of the 6 classes:


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o P: palm oil, palm olein and palm stearin

o RS: rapeseed oil, sunflower oil, rapeseed and sunflower oil admixture

o PKOC: palm kernel oil, coconut oil

o RSPKOC: rapeseed and palm kernel oil admixture, sunflower and palm kernel oil admixture

o RSP: rapeseed and palm oil admixture, sunflower and palm oil admixture

o PPKOC: palm oil and palm kernel oil admixture, palm oil and coconut oil admixture

Open MATLAB.

Signal Processing Toolbox is needed in order to run this prediction tool .To check if this

toolbox is installed go Home -> Add-Ons -> Manage Add-Ons and in the window opened

find the Signal Processing Toolbox. If it is not installed, please install this.

Download MATLAB models.zip from the QUB website, unzip and copy to MATLAB

working folder.


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Select the folder in the working path.

Type predict in the command window.

In the pop-up window appearing select ‘Add spectra’ and locate the excel file that contains

one or more FTIR spectra formatted as seen in Section 8.1.1 and click ‘Open’. Tool will read

the data in the first worksheet of the Excel file.

If successful, the selected filename is displayed in the pop up window and then click ‘Predict

Oils’.

Incorrect files will return an error. Some potential error messages are:


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- ‘Please check the contents of the excel file. Try again’: When you have added some chars by

fault in the absorbance values of a spectrum.

- ‘The number of wavenumbers is not equal with the number of the spectra values. Try again’ :

When the absorbance values of a spectrum are not equal to the number of the wavenumbers.

- ‘First row of the excel file has to include the FTIR wavenumbers. Try again’: If the first row

of the excel datasheet does not include the wavenumbers.

A message in the window informs that the user has to be waiting because the prediction is in

progress.

Once the prediction is finished, a pop-up window including the classification list will appear.


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The predicted class for each sample will be displayed in the second column. The classification list

shows the probability that an observation belongs to a class. A cell will be marked green if the value

is above 0.7, orange if the value is between 0.5 and 0.7 and white below 0.5.

All samples are therefore categorised in 3 well defined groups:

Samples with high certainty (probability >0.7) to belong in the particular class are marked

green.

Samples with medium certainty (0.5 =< probability < 0.7) are marked orange.

Samples appear white because the probability to belong to the particular class is low (< 0.5).

Samples predicted with this probability are forwarding to the confirmation step (Red

coloured predicted classes).

High resolution determination of the oil species in an unknown oil blend using the 12 classes’ model

(Model C – new model)

Model C is a calibration model built with PLS-DA using SIMCA Umetrics software. The model is

able to predict the identity of unknown oil samples assigning the unknown samples to one of the 12

classes:

o P: palm oil, palm olein and palm stearin

o RO: rapeseed oil

o SO: sunflower oil

o PKO: palm kernel oil

o CCO: coconut oil

o ROPO: rapeseed and palm oil admixture

o SOPO: sunflower and palm oil admixture

o ROPKO: rapeseed and palm kernel oil admixture

o SOPKO: sunflower and palm kernel oil admixture

o ROSO: rapeseed and sunflower oil admixture

o PPKO: palm oil and palm kernel oil admixture

o PCCO: palm oil and coconut oil admixture

The PLS-DA calibration model for the 12 classes’ model is saved in a USP filename on the QUB

website freely available to download.


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The calibration model was built using pre-processed FTIR spectral data and the same pre-processed

techniques will be automatically applied to the incoming spectral data from unknown samples

without any user action.

Open SIMCA 14.0 Umetrics™

Click ‘File’ and then ‘Import Dataset’. Choose the excel file with the spectral data of the

testing/unknown samples and click ‘Open’.

Define the Primary ID for the observations (rows) and the Primary ID for the variables

(columns) as shown below:

Click ‘Finish’.

Under the tab ‘Predict’, click ‘Specify’ and the following window will appear:


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Select the Prediction dataset that was previously imported in the drop-down menu of the

option ‘Source’. Enter a name for the new testing set at the bottom of the window. Click

‘Apply’ and then ‘OK’.

Click the option ‘Classification list’ under the ‘Predict’ tab to obtain a table of the

classification of the samples included in the testing set according to the calibration model.

The classification list shows the predicted dummy variable (YPredPS). The observations are

coloured according to the predicted values:


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Not classified: < 0.35 are white (do not belong to the class).

Medium certainty: between 0.35 and 0.65 are orange (borderline).

High certainty: above 0.65 are green (belong to the class).

Membership of a class depends upon matching the value of the dummy variable, so a value close to

one indicates membership to a class. In practice 0.5 is often used as a practical threshold in order to

classify an observation as belonging to one class or another. A threshold of 0.54 was selected for this

specific model (Model C with thresholds-12 classes).

Determination of oil species in a biscuit product using the Biscuit-only model

The biscuit-only model is a calibration model built with PLS-DA using SIMCA Umetrics™

software. The model is able to predict the identity of unknown oil samples extracted from biscuits

assigning them to one of the 3 classes:

o P: palm oil

o PORO: rapeseed and palm oil admixture

o RO: rapeseed oil

The PLS-DA calibration model for the biscuit-only model are saved in a USP filename on the QUB

website freely available to download.

The calibration model was built using pre-processed FTIR spectral data and the same pre-processed

techniques will be automatically applied to the incoming spectral data from unknown samples

without requiring any user action.

Open SIMCA 14.0 Umetrics™

Click ‘File’ and then ‘Import Dataset’. Choose the excel file with the spectral data of the

testing/unknown samples and click Open.

Define the Primary ID for the observations (rows) and the Primary ID for the variables

(columns).

Click ‘Finish’.

Under the tab ‘Predict’, click ‘Specify’.

Select the Prediction dataset that was previously imported in the drop-down menu of the

option ‘Source’. Enter a name for the new testing set at the bottom of the window. Click

‘Apply’ and then ‘OK’.


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Click the option ‘Classification list’ under the ‘Predict’ tab to obtain a table of the

classification of the samples included in the testing set according to the calibration model.

The classification list shows the predicted dummy variable (YPredPS). The observations are

coloured according to the predicted values:

Not classified: < 0.35 are white (do not belong to the class).

Medium certainty: between 0.35 and 0.65 are orange (borderline).

High certainty: above 0.65 are green (belong to the class).

Membership of a class depends upon matching the value of the dummy variable, so a value close to

one indicates membership to a class. In practice 0.5 is often used as a practical threshold in order to

classify an observation as belonging to one class or another. A threshold of 0.70 was selected for this

specific model (Biscuit-only model with thresholds-3 classes).

Detection of the presence of palm oil species in an unknown oil sample extracted from a

confectionery product using the Confectionary-only model.

Confectionary-only model is a calibration model built with PLS-DA using MATLAB. The model is

able to detect the presence or absence of palm oil especies of unknown oil samples extracted from

confectionery products assigning the unknown samples to one of the 2 classes.

o P: palm oil, palm olein, palm kernel oil, hydrogenated palm kernel oil and the oil admixtures

SO+PO, PO+PKO, CB+PO, CB+PO+SB, CB+PO+ShB, CB+PO+SB+ShB and

CB+PO+SB+ShB+ILB+KMB+MNB.

o Non-P: cocoa butter


Page 25 of 32

Open MATLAB. MATLAB is a commercial software supplied from Mathworks. Version

2009 or newer is recommended.

Signal Processing Toolbox is needed in order to run this prediction tool .To check if this

toolbox is installed go Home -> Add-Ons -> Manage Add-Ons and in the window opened

find the Signal Processing Toolbox. If it is not installed, please install this.

Download MATLAB models.zip from the QUB website, unzip and copy to MATLAB

working folder.

Select in the working path the folder.

Type predict in the command window.


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In the pop-up window appearing select ‘Add spectra’ and locate the excel file that contains

one or more FTIR spectra formatted as per Section 8.1.1 and click ‘Open’. Tool will read the

data in the first worksheet of the Excel file.

If successful, the selected filename is displayed in the pop up window and then click ‘Predict

Confectionery’.

Incorrect files will return an error. Some potential error messages are:

- ‘Please check the contents of the excel file. Try again’: When you have added some chars by

fault in the absorbance values of a spectrum.

- ‘The number of wavenumbers is not equal with the number of the spectra values. Try again’:

When the absorbance values of a spectrum are not equal to the number of the wavenumbers.

- ‘First row of the excel file has to include the FTIR wavenumbers. Try again’: If the first row

of the excel datasheet does not include the wavenumbers.


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A message in the window informs that the user has to be waiting because the prediction is in

progress.

Once the prediction is finished, a pop-up window including the classification list will appear.

The predicted class for each sample will be displayed in the second column. The classification list

shows the probability that an observation belongs to a class. A cell will be marked green if the value

is above 0.7, orange if the value is between 0.5 and 0.7 and white below 0.5.

All samples are therefore categorised in 3 well defined groups:

Samples with high certainty (probability >0.7) to belong in the particular class are marked

green.

Samples with medium certainty (0.5 =< probability < 0.7) are marked orange.


Page 28 of 32

Samples appear white because the probability to belong to the particular class is low (< 0.5).

Samples predicted with this probability are forwarding to the confirmation step (Not

classified, red coloured predicted classes).

8.2 Confirmation step based on chromatographic data (fatty acid by GC)

Referral of samples from the screening step

Only samples with probabilities/predicted dummy variables (YPredPS) below these thresholds are

referred to the next stage (confirmation step). The established threshold for model B (6 classes’

model) is 0.5 and for model C (12 classes’ model) is 0.54.

Chromatographic analysis of fatty acids by GC needs to be performed on the unidentified samples

following the protocol described in Section 7.4.

The referral procedure for the model B and confectionery-only model is:

The referral procedure for the model C is:


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The referral procedure for the Biscuit-only model is:

Calculations of fatty acid content (mg FA/g)

Fatty acid contents are calculated as follow:

Individual fatty acid concentrations are calculated using the internal standard method. Response

factors are calculated from the mixed standard with respect to C13:0 which is used as the internal

standard.

The areas of the peaks of all chromatograms are placed in an excel file as below:

The peak area of the individual fatty acid is divided by the peak area of the internal standard,

multiplied by the internal standard concentration and then by the corresponding response factor and

then applying sample weight and dilution factors. Duplicate analyses are then averaged.

The formula followed for calculation is as follow:


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Where……… FA: fatty acid and

IS: internal standard

and the units used are: Conc IS = mg IS/ml

Dilution = ml

Sample weight = g

Final results are expressed as mg fatty acid/g of sample.

Note: P/S ratio is an index of the polyunsaturated character of the oil and it is calculated using most

of the FA contents according to the formula below:

C18:2 + C18:2 isomers + C18:3 n3 + C18:3 n6

P/S ratio = ----------------------------------------------------------------------------------

C8:0 + C12:0 + C14:0 + C16:0 + C18:0 + C20:0 + C22:0 + C24:0

Fatty acid classification criteria of an unknown sample

The criteria for the 6 and 12 classes’ models are shown in Table 2 and Table 3, respectively. These

criteria are applied for the identification of an unknown sample (oil blends and oils extracted from

biscuit/pastry products) as follows:

The criteria are applied to confirm if the unknown oil blend belongs to one of the known classes (P,

PKOC, RS, PPKOC, RSP and RSPKOC for the 6 classes’ model and PKO, RO, SO, P, ROSO,

ROPKO, SOPKO, ROPO, SOPO, PPKO, PCCO and CCO for the 12 classes’ model). All conditions

have to be met for a sample to belong in a class. This is applied in all classes.

If the unknown sample meets the criteria of a specific class it is classified in the corresponding class.


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Table 2. Criteria expressed in quantities (mg fatty acid/g oil) for 6 classes’ model (Model B).

* P group: palm oil, palm stearin, palm olein; PKOC group: palm kernel oil, coconut oil; RS group: rapeseed oil,

sunflower oil, rapeseed and sunflower admixtures; RSP group: RS group + P group; PPKOC group: P group + PKOC

group; RSPKOC group: RS group + PKOC. FA: Fatty acid; PUFA: Polyunsaturated fatty acids; SAT: Saturated fatty

acids; P/S: Polyunsaturated fatty acids/Saturated fatty acids

Table 3. Criteria expressed in quantities (mg fatty acid/g oil) for 12 classes’ model (Model C).

Class

FA

PKO RO SO P ROSO ROPKO SOPKO ROPO SOPO PPKO PCCO CCO

C6:0 <3 0 0 0 0 0 0 <1.0

0.1-

2.5 >1.0

C8:0 5.0-40 0 0 0 0 <15 <15 0 0 <15 3.0-35 25-50

C10:0 10-

30.0 0 0 0 0 <15 <15 0 0 <20 3.0-35 25-50

C12:0 150-

400 0 0 >0.5 0 <235 <235

0.02-

1.5

0.01-

1.25 <250

20-

275 250-350

C14:0 5-10 <10 <10 <100

15-

125 >100

C16:0 50-

100 20-50

30-

70 >300 20-60 <70 <70

20-

400

50-

400

50-

400

100-

325 50-100

C16:1 0

0.5-

1.5

C18:0 <25

5.0-

15

15-

35

20-

45 5.0-30 <25 <25 5.0-35 20-40 15-35 20-35 15-30

C18:1c 80-

175

20-

600

150

-

250

150-

400

200-

600

100-

600

100-

250

200-

600

150-

350

125-

300

80-

250 40-80

C18:2c

<30 75-

175

300

-

550

40-

85

100-

450 15-175 50-400

50-

175

50-

450 15-75 15-60 5.0-35

C18:3c9,1

2,15

30-

100 <3 <75 2.0-75 0.1-2.0 2.0-90 0.5-2

PUFA/

SAT <0.07

2.0-

4.5

4.5-

6.0

<0.2

7

3.0-

6.0 <2.75 <4 <3.25 <5.0 <0.16 <0.16 <0.075

Specific FA

(mg FA/g oil) P PKOC RS PPKOC RSP RSPKOC

C8:0

Caprylic acid >8 >2.5 >2.5

C12:0

Lauric acid >0.99 >150 <0.1

C14:0

Myristic acid 5.8-10.0 <0.7

C16:0

Palmitic acid 315-490 50-100 >=70 58-330 35-70

C18:1

oleic acid >=195

C18:2

Linoleic acid 43-85 <35 135-550 25-75 70-425 24-450

PUFA /SAT

(P/S) ratio <0.25 <0.06 >3.5 <=0.3 >=0.325


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* FA: fatty acid; PKO: palm kernel oil; RO: rapeseed oil; SO: sunflower oil; P: palm oil, palm olein and palm stearin;

ROSO: rapeseed and sunflower oil admixture; ROPKO: rapeseed and palm kernel oil admixture; SOPKO: sunflower

and palm kernel oil admixture; ROPO: rapeseed and palm oil admixture; SOPO: sunflower and palm oil admixture;

PPKO: palm oil and palm kernel oil admixture; PCCO: palm oil and coconut oil admixture; CCO: coconut oil;

PUFA/SAT: polyunsaturated fatty acids/Saturated fatty acids

The criteria for the detection of palm oil species in a chocolate confectionery product are shown in

Table 4. These criteria are applied for the confirmation of the absence of palm oil species in an

unknown sample (oil extracted from confectionery products). All conditions have to be met for

confirming the absence of palm oil species in an unknown sample.

Table 4. Fatty acid criteria for confectionery chocolate products for the Confectionery-only Model

Specific FA (mg FA/g oil) Pure Cocoa

butter

C16:0 Palmitic acid <250

C18:0 Stearic acid >200

PUFA /SAT (P/S) ratio <0.048

* FA: fatty acid; PUFA/SAT (P/S): polyunsaturated fatty acids/saturated fatty acids.

9. RELATED PROCEDURES

Not applied.

10. ESSENTIAL REFERENCES

BS 684-2.34:2001. Animal and vegetable fats and oils. Preparation of esters of fatty acids.

Section 5 Trans-esterification method, pp-7-9, BSI London.

11. APPENDICES

Not applied

---------- END OF DOCUMENT ----------

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