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A key advantage of the Direct Analysis in Real Time (DART) mass spectrometry (MS) ion source is its ability to ionise the sample without the need for extraction.  In this study (open access), the authors compared DART with a previously-published extraction-based MS method to analyse key components in olive oil.

Having optimised and validated DART-MS, they then used it to build a discrimination model between different classes of edible oils.  They analysed a reference set of 80 samples from different regions of Greece (Crete, Peloponnese, Central Greece, and the North Aegean) to discriminate authentic extra virgin olive oil (EVOO).  These were from 10 oil categories including 35 EVOOs, 15 lower-quality olive oils (five of each category: refined, olive pomace, and ordinary), and 30 vegetable oils (five of each type: sunflower, corn, soybean, canola, sesame, and linseed).

They report that multivariate statistical analysis revealed clear discrimination of EVOO from other oils and enabled detection of EVOO adulteration down to 1 % with vegetable oils and 5 % with lower-quality olive oils. Key authenticity markers, including phenols, squalene, and triacylglycerols (TAGs), were identified.

They conclude that the proposed method demonstrates high potential for rapid, reliable EVOO authentication in routine quality control.

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.

Photo by Massimo Adami on Unsplash

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) two optical spectroscopic techniques,  Laser-Induced Breakdown Spectroscopy (LIBS) and UV-Vis-NIR absorption spectroscopy, are assessed for EVOO adulteration detection, using the same reference database of olive oil samples. In total, 184 samples were studied, including 40 EVOOs and 144 binary mixtures with pomace, soybean, corn, and sunflower oils, at various concentrations (ranging from 10 to 90% w/w). The reference class of “pure” EVOOs were limited to oils from a specific geographic region (either Crete, Lesvos, Kalamata or Achaia, with a different model built for each case).

The emission data from LIBS, related to the elemental composition of the samples, and the UV-Vis-NIR absorption spectra, related to the organic ingredients content, were analyzed, both separately and combined (i.e., fused), by Linear Discriminant Analysis (LDA), Support Vector Machines (SVMs), and Logistic Regression (LR). In all cases, very highly predictive accuracies were achieved, attaining, in some cases, 100%.

The authors conclude that both techniques have the potential for efficient and accurate olive oil verification test protocols, with the LIBS technique being better suited as it can operate much faster.