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%.
