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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.

Photo by Pranjall Kumar on Unsplash

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%.