Classification of olive oils according to sensory defects using a potentiometric electronic tongue

Abstract

Olive oil is a highly appreciated food product being very prone to frauds. Olive oils may be graded as extra-virgin, virgin or lampante. This classification is attributed according to legal requirements, including chemical parameters and sensorial analysis. Among the organoleptic sensations, the capability of perceiving the presence or absence of sensory defects plays a key role for olive oils grade classification. This task is time-consuming and quite expensive, requiring the use of an official taste panel, which can only evaluate a low number of samples per day. In this work, an electronic tongue is proposed to discriminate olive oils according to the defect predominantly perceived (winey-vinegary, wet-wood, rancid and fusty/muddy sediment), by a trained sensory panel. Sub-sets of potentiometric signal profiles obtained from the lipid sensor membranes of the taste electrochemical device were selected using a simulated annealing meta-heuristic algorithm, allowing establishing classification linear discriminant model, which showed a predictive success classification rate of 81% for leave-one-out or cross-validation procedure. The satisfactory predictive performance achieved pointed out the practical potential of using this artificial taste sensor as a complementary methodology for olive oil sensory analysis.