Estimation of K Value and Free Fatty Acids of Adulterated Olive Oil Using Fluorescence Spectroscopy Coupled with Multivariate Analysis and Convolutional Neural Network Models

Abstract

Adulterating extra-virgin olive oils (EVOO) with lower grade olive oils, like virgin olive oil (VOO), and selling it as EVOO to unsuspecting consumers has sparked concern in the recent years. Developing inexpensive and quick adulteration detection methods to unravel such acts will promote trust in the industry. This study focused on the quality degradation of EVOO when adulterated by different proportions of VOO. Excitation emission matrices (EEMs) and fluorescence images were taken for analysis. Partial least square regression (PLSR), support vector machine (SVM), decision tree and convolutional neural network (CNN) models were used to explore both the EEMs and fluorescence images of adulterated oils, which indicate the extent of adulteration of extra virgin olive oils can be detected.