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This paper (open access) is an example of the growing trend of “data fusion” from multiple techniques to aid authenticity classification.  Contributing techniques can be extremely simple.  In this case the researchers including Differential Scanning Calorimetry (DSC), which is a routine way to measure the melting point of a solid (or the crystallisation point of an oil).  They combined DSC with GC-MS profiling of fatty acid methyl esters (FAMEs) to detect adulteration of walnut oil.

The researchers prepared cold-pressed walnut oils in an normal manufacturing setting, in compliance with the Romania legal specification constraints.  They produced binary mixtures adulterated with sunflower oil between 5 – 50 %.

They report that, as the proportion of adulterant oil increased, thermal parameters changed progressively. Samples with 40% and 50% sunflower oil exhibited low crystallisation enthalpy values, reflecting the dominant thermal characteristics of sunflower oil at higher substitution levels. Chemometric analysis demonstrated strong discrimination between authentic and adulterated oils, with support vector machine models achieving complete classification under the conditions of this study.

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This review (open access) evaluates isomer-resolved lipid fingerprints as a complementary approach to established screening methods to detect edible oil adulteration.  The review focusses on two underexploited structural dimensions: (i) double-bond positional and geometric isomerism and (ii) triacylglycerol (TAG) sn-regioisomerism, which encode biosynthetic specificity and processing “memory” that is less accessible to conventional compositional markers.

The authors compare analytical strategies (e.g., epoxidation-MS/MS and Paternò–Büchi reactions, ozone-based dissociation/ozonolysis, and ion mobility–mass spectrometry) highlighting their respective strengths, limitations, need for derivatisation, and fit-for-purpose roles in food-industry contexts.

The authors describe some specific examples relating to high-value oil adulteration, differentiation of native versus refined/reprocessed products, monitoring of thermal/oxidative history, and emerging nutrition-relevant structure–function questions. They recommend improvements and standardisation relating to reporting confidence and nomenclature, quantitation and reference materials, tiered workflows (screening-to-confirmatory), and defensible decision thresholds.  They identify key gaps in inter-laboratory comparability, controlled processing studies, and food-specific data infrastructure.

Undeclared lard in confectionary products is a significant concern for consumers in many parts of the world who avoid pork on religious grounds.

This study (GBP30 download fee) used gas chromatography with flame ionization detection (GC–FID) to measure fatty acids, then principal component analysis (PCA) to detect porcine fatty acid biomarkers in imported chocolates and biscuits.

The authors report that total fat content ranged from 11.5 to 32.5%, with palm kernel-based chocolates enriched in lauric (42–52%) and myristic acids (18–20%), while other chocolates were dominated by palmitic, stearic, and oleic acids. Biscuits contained high proportions of palmitic and oleic acids (> 75%).

PCA of the complete fatty acid dataset separated lard-adulterated samples.. Targeted PCA using porcine biomarkers palmitic-to-oleic acid ratio and eicosadienoic acid confirmed this clustering.

Calibration using simulated lard–palm oil mixtures (0–15% w/w; five replicates per level) enabled quantitative estimation of lard .

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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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This Masters’ degree project developed a predictive algorithm to categorize butter, butter spreads, and margarine/vegetable oil spreads according to their fatty acid profile, moisture, and total fat content based on the spectra collected by using handheld FT-NIR and portable FT-MIR devices. FT-NIR infrared and FT-MIR performances were similar, with a strong correlation (Rep >0.94) and low standard error of prediction for different analyzed parameters. SIMCA classification model based on FT-NIR and FT-MIR spectra effectively differentiated between butter, butter spread, and margarine/vegetable oil spreads.

The results were benchmarked against “classical” analysis.  Moisture and total fat content were determined using reference methods AOAC 920.116 and AOAC 938.06-1938, respectively. FA profile was determined using Gas Chromatography with flame ionization detector (GC-FID) (AOAC 996.06, 1996.). The FA profile showed that butter-containing products distinguished from margarine/vegetable oil spreads based on the presence of trans fats (TFA) (C18:1t) and butyric acid (C4:0).

The author concludes that portable FT-MIR and handheld FT-NIR technologies offer real-time and in situ analysis capabilities, enabling the dairy industry and regulatory agencies to make actionable decisions regarding FA, moisture, and total fat content and for nutrition, authentication, claims, and labeling purposes of these products.

The abstract and author contact details are available here.  The full text is being withheld until May 2026 at the author’s request. For an overview of FT-NIR see FAN's analytical techniques explainer for spectroscopy.

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In this study (purchase required) the researchers build a classification model for differentiate freshwater from seawater shrimp (prawns), Litopenaeus vannamei, based on fatty acid (FA) profiling in muscle and hepatopancreas.

They built an untargeted model, using k-nearest neighbor (KNN) and random forest (RF), to identify discriminatory variables.

They then identified, using orthogonal partial least squares-discriminant analysis (OPLS-DA) specific FAs to create their classification model: six (C22:6n3, C20:3n3, C17:0, C18:3n3, C20:5n3, and C20:2) from the muscle and seven (C22:6n3, C16:0, C18:3n3, C18:2n6, C20:2, C20:1, and C18:1n9) from the hepatopancreas.

They report that, using FA profiles from the two tissues, both KNN and RF had initial and cross-validated classification rates >93%, while the predictive classification rates of the models based on muscle FA profiles were higher than that of the models based on hepatopancreas FA profiles. They conclude, therefore, that FA profiles in muscle were more effective than hepatopancreas FAs for this promising classification method.

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