A classification and identification model of extra virgin olive oil adulterated with other edible oils based on pigment compositions and support vector machine

Abstract

Adulteration identification of extra virgin olive oil (EVOO) is a vital issue in the olive oil industry. In this study, chromatographic fingerprint data of pigments combined with machine learning methodologies were successfully identified and classified EVOO, refined-pomace olive oil (R-POO), rapeseed oil (RO), soybean oil (SO), peanut oil (PO), sunflower oil (SFO), flaxseed oil (FO), corn oil (CO), extra virgin olive oil adulterated with rapeseed oil (EVOO-RO) and extra virgin olive oil adulterated with corn oil (EVOO-CO). Support vector machine (SVM) classification of EVOO, other edible oils, and EVOO adulteration identification achieved 100% accuracy for the training set sample and 94.44% accuracy for the test set sample. As a result, this SVM model could identify effectively the adulteration EVOO with the limit of 1% RO and 1% CO. Therefore, the excellent classification and predictive power of this model indicated pigments could be used as potential markers for identifying EVOO adulteration.

Keywords: Adulteration; Classification; EVOO; Identification; Pigments; SVM.