Authentication of Extra Virgin Olive Oil (EVOO) sometimes requires a panel of different tests and – with more sophisticated adulteration – a weight of evidence interpretation.  For more crude adulterations a single test is often enough.

One of the available tests is for fatty acids ethyl esters (FAEE).  These are more concentrated in lower quality oils (e.g.improperly stored or overripe), formed from ethanol which is a result of fermentation. EU legislation specifies a maximum 35 mg per kg FAEE concentration in EVOO.

FAEE concentration is officially measured using gas chromatography (GC) after recovery by silica gel column chromatography. While highly accurate, this method is complex, time-consuming, and relatively expensive.

This paper (purchase required) reports an alternative approach to FAEE measurement by using infra-red spectroscopy (FT-IR) with machine learning. A dataset of 170 olive oil samples with FAEE concentrations ranging from 1.81 mg/kg to 109.00 mg/kg were analysed using FTIR. Spectral data were preprocessed and used to train various regression models.

The authors report that the best performance was obtained with an XGBoost model. Explainable AI techniques (SHAP) enabled interpretation of the model and identification of spectral regions mostly associated with FAEE content.

They conclude that combining FT-IR spectroscopy with advanced ML models—particularly XGBoost—can effectively predict the concentration of FAEE.

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Classification models for food authenticity tests can - in principle -  be based on any analytical technique that collects multi-variate data.  In the case of spectrometric data (such as NIR or multi-spectral imaging) the equipment can be relatively cheap.  For collecting chemical data, researchers often use high-end equipment such as advanced LC-MS or GC-MS

This proof-of-concept study (purchase required) is a rare example of building a classification model using a cheaper test (HPLC with fluorescence detection) to measure a chemical parameter.  The authors prepared cold-pressed walnut and pumpkin seed oils adulterated with 0 – 50% of sunflower oil.  They developed a classification model based on the concentrations of the four tocopherols (α-, β-, γ-, and δ-).  They report that the model was capable of discriminating sunflower oil adulteration down to 2-3%.

A Belgian local newspaper has conducted a survey of 32 samples of branded Extra Virgin Olive Oil sold through major retail outlets iand supermarkets.  The brands include internationally-recognised household names.  The newspaper commissioned testing at expert laboratories.

Results are summarised in this press article.  20 of the 32 “Extra Virgin” samples failed to meet the specification standard of Extra Virgin Olive Oil (EVOO).  Although oil degradation over time could be a hypothesis in some cases, in other cases the analytical weight of evidence was that the oil was Lampeter (a lower grade of olive oil) or – in one case – adulterated with sunflower oil.

Investigations are continuing into suspected certification fraud in the upstream supply chain.

In this paper (open access) the authors  propose two novel metrics—the Geographical Differentiation Index (GDI) and Environmental Heritability Index (EHI)—to quantify spatial variation in fatty acids and their environmental drivers. These methodologies are derived from classical genetic theory - traditional heritability quantifies the contribution of genes to traits by calculating the ratio of additive genetic variance to phenotypic variance.  The authors applied this same methodology to the fatty acid profile of oils, in order to diagnose their geographic origin.

They systematically investigated the fatty acid profiles of four main oil-rich crops (olive, camellia, walnut, and peony seed) and revealed that fatty acid distributions follow elevation- and latitude-dependent patterns, with peony seed oils showing the strongest latitudinal sensitivity. Key fatty acids like stearic acid (C18:0) and linoleic acid (C18:2) correlated significantly with geographic factors globally, while the biomass of certain specific fatty acids varies significantly in high-altitude/low-latitude regions. They conclude that their findings establish specific fatty acid signatures as a robust tool for geographic authentication. They provide a chemical rationale for classification models, based on Machine Learning, that measure differences in fatty acid profiles.

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The authors of this study (purchase required) propose a radio frequency (RF)-based sensing method that operates in the 6.22 GHz frequency range as a method to authenticate edible oils. In order to obtain a return loss below -10 dB within the desired frequency range, their sensor makes use of a microstrip patch antenna with triangular slots and a microfluidic channel that has been adapted by parametric variations.

They tested the concept with in-house preparations of olive oil which were then adulterated with increasing quantities of coconut and mustard oils.  Results were correlated with GC-MS.  They report that the sensor's measured sensitivity for identifying oil adulteration is 0.18, and conclude that this demonstrates proof of concept for using an RF sensor as a quick method of verifying vegetable edible oils.

Cold-pressed fruit seed oils from blackcurrant, raspberry, and strawberry are gaining market share and – as relatively high value oils – are potential targets for adulteration. This study (open access) used identified 28 triacylglycerides (TAGs) as significant markers for distinguishing the 3 oils.  These were identified from chemometric analysis of full tryglyceride profiles.  Triglycerides were measured by ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry. Lipidomic analysis identified 215 glycerides in the three oils. Chemometric analysis revealed that TAG profiles were superior to diacetylglyceride (DAG) profiles for oil differentiation and detecting adulteration. OPLS-DA identified 28 TAGs as significant markers for distinguishing the three oils.

The authors reported that comparison of glyceride profiles of pure and adulterated samples demonstrated that adulteration with 5 % or more sunflower or rapeseed oil could be detected. Targeted metabolomic analysis using specific markers for sunflower oil confirmed adulteration in raspberry and strawberry commercially purchased fruit seed oils.

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This study (purchase required) explores the ratio between the values of the saponification and iodine indices in edible oils.  The relationship is used as the basis of a classification model. The approach is termed a Quantitative Structure-Property Relationship (QSPR) study..

The authors report that QSPRs were formulated in 144 vegetable oils, composed of 1–8 fatty acid components. Details of the varieties and sources used for the training and validation sets are not available in the online abstract.  A set of 25,118 mixture descriptors was calculated as linear combinations of the non-conformational descriptors of the fatty acid components and their weight percent compositions. This approach was found useful for discerning natural oils.  The Replacement Method variable subset selection technique was applied afterwards to select the best mixture descriptors in the predictive model.

To test the model, different vegetable oils with known composition, but unknown experimental saponification and iodine indices data, were successfully classified using the established QSPR.

The authors conclude that two-variable QSPR analysis can be extended to fats and other types of oils, such as fish oils. It also serves as a background and database for other methodologies.

Most test methods and research into the authenticity of edible oils are focussed on differentiating different plant species or on different grades of olive oil.  There has been relatively little focus on different grades of sunflower oil.  Commercial sunflower oil is sold as three different grades with increasing price premium; standard Sunflower Oil (SFO), Medium Oleic Acid (MOSFO) and High Oleic Acid (HOFSO).  HOFSO is more stable to repeated heating/cooling cycles and so is the grade typically required for fast food restaurants.  It is also available as a premium product sold direct to consumers.

In this paper (open access) the researchers used Spatially Offset Raman Spectrocopy (SORS, a portable non-invasive sensor) to build statistical models that could differentiate HOFSO from those that were not HOFSO (i.e. either MOSFO or SFO).  Although the reference samples used to build the model were purchased from commercial outlets rather than being of verified authenticity, the fact that two different unsupervised mathematical plus a number of supervised approaches all led to similar classification models, and that the models were validated with samples independent of the training sets, gave increased confidence in the model.

The authors conclude that the use of  SORS in combination with the developed chemometric models is an effective tool for the HOSFO authentication. The approach is simple and rapid, with instrumental fingerprints from portable analyser in less than 2 min and without requiring sample preparation.  This approach would class as Green Analytical Chemistry.

Image from the paper

Low Field (LF) Nuclear Magnetic Resonance (NMR) spectrometers have been used for routine inline quality control in the processed meat industry for many years.   Recent advancements have made the technology more accessible for other applications.

This proof-of-concept study (open access) demonstrates the potential of LF NMR for rapid oil authentication in an industrial setting. Their approach was based on solvent-free oil analysis using a single scan ¹H NMR measurement on a LF 80 MHz NMR instrument. The analysis identified the allylic signals at δ 2.0 ppm as a potential diagnostic region, effective in detecting adulteration in the oil samples. They limited the integration to this one spectral region in order to make data analysis rapid and easy to use in a food factory.

The authors demonstrated successful detection of adulteration in two types of vegetable oils rich in polyunsaturated fatty acids (PUFA), rapeseed and sunflower oil, at levels ranging from 5 % to 25 %. Specifically, the study found that adulteration in rapeseed oil could be detected at levels as low as 5 % when adulterated with soybean oil, 10 % when adulterated with sesame and cottonseed oils, and 25 % for corn oil and safflower oil. In the case of sunflower oil, cottonseed oil can be identified at 5 % adulteration, while corn, sesame, and safflower oils can be detected at 25 % adulteration.

The authors consider that the approach is fast, user-friendly, and ecological.  LF NMR could be a valuable tool for identifying adulteration in edible oils, with applications in various industries. This method would benefit from further research to validate the allylic region as a diagnostic region of oil adulteration.

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FSA-funded project: Review of current and emerging analytical methods for the testing of oil for authenticity (Project FS900520)

With funding from the UK Food Standards Agency, Fera Science Limited (Fera) in York, UK is currently undertaking a project to review the current and emerging analytical methods for testing edible oils and support the further development of analytical methods which will underpin and uphold the authenticity of edible oils in the supply chain. 

As part of the project’s evidence gathering, Fera would like to invite parties involved in sourcing, processing, and/or testing edible oils to participate in an online questionnaire. 

The fundamental mission of the FSA is food you can trust. The FSA strategy sets out FSA’s vision to ensure that the UK food system is safe, and that food is what it says it is. This involves building scientific capability in Public Analyst (PA) Official Laboratories (OLs) and working with Defra’s food authenticity programme to conduct research and development for analytical methods. Suitable analytical methods are required to ensure that food is what it says it is and to manage risk around food authenticity.

 As key stakeholders, your insight will help to inform FSA regarding issues in oil authenticity and future-proofed analytical tools to support both industry and regulators, while maintaining consumer confidence in our food. 

 Your participation will be very much appreciated and your views and insight will be invaluable to the project aims.

 A summary of key findings from the questionnaire will be included in the final report, but no sensitive information will be published.

Please complete the questionnaire here. If you have any questions, please contact info@fera.co.uk.

Your kind participation will be very much appreciated and your views and insight will be invaluable to the project aims.

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DNA analysis is rarely used for the verification of edible oil species, because of the low amount of intact DNA in the refined oil and the genetic similarities between different oil varieties.  In this study (open access pre-print, not yet peer-reviewed) the authors compared different DNA extraction kits and PCR protocols and new genetic markers to try and resolve the issue.  They reported that DNA extraction kits such as NucleoSpin Food, DNeasy mericon Food, and Olive Oil DNA Isolation as well as modified CTAB method were found to be able to isolate amplifiable genomic DNA from highly processed oils. Novel uniplex, double, and nested PCR systems targeting the sunflower-specific helianthinin gene were developed for efficient identification of sunflower. New sunflower DNA markers were revealed by uniplex PCRs.

They concluded that a combination of modified CTAB and nested PCR gave the best performance, and was demonstrated as a reliable, rapid, and cost-effective technology for detecting traces of sunflower in highly processed oil, including refined and used cooking oil.