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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)
The methodology employed is a staged procedure that consists of a combination of FTIR
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
The methodology employed is a staged procedure that consists of a combination of FTIR
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.
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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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2.0°C /min to 225°C, hold for 10.0 minutes.
20.0°C /min to 240°C, hold for 5.0 minutes.
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
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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.
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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
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