Application of an electronic tongue for Tunisian olive oils’ classification according to olive cultivar or physicochemical parameters

Olive oil commercialization has a great impact on the economy of several countries, namely Tunisia, being prone to frauds. Therefore, it is important to establish analytical techniques to ensure labeling correctness concerning olive oil quality and olive cultivar. Traditional analytical techniques are quite expensive, time consuming and hardly applied in situ, considering the harsh environments of the olive industry. In this work, the feasibility of applying a potentiometric electronic tongue with cross-sensitivity lipid membranes to discriminate Tunisian olive oils according to their quality level (i.e., extra virgin, virgin or lampante olive oils) or autochthonous olive cultivar (i.e., cv Chétoui and cv Shali) was evaluated for the first time. Linear discrimination analysis coupled with the simulated annealing variable selection algorithm showed that the signal profiles of olive oils’ hydroethanolic extracts allowed olive oils discrimination according to physicochemical quality level (classification model based on 25 signals enabling 84 ± 9% correct classifications for repeated K-fold cross-validation), and olive cultivar (classification model based on 20 signals with an average sensitivity of 94 ± 6% for repeated K-fold cross-validation), regardless of the geographical origin and olive variety or the olive quality, respectively. The results confirmed, for the first time, the potential discrimination of the electronic tongue, attributed to the observed quantitative response (sensitivities ranging from -66.6 to +57.7 mV/decade) of the E-tongue multi-sensors towards standard solutions of polar compounds (aldehydes, esters and alcohols) usually found in olive oils and that are related to their sensory positive attributes like green and fruity.This work was financially supported by Project POCI-01–0145-FEDER-006984–Associate Laboratory LSRE-LCM and by Project UID/QUI/00616/2013–CQ-VR both funded by FEDER—Fundo Europeu de Desenvolvimento Regional through COMPETE2020-Programa Operacional Competitividade e Internacionalização (POCI)—and by national funds through FCT-Fundação para a Ciência e a Tecnologia, Portugal. Strategic funding of UID/BIO/04469/2013 unit is also acknowledged. Nuno Rodrigues thanks FCT, POPH-QREN and FSE for the Ph.D. Grant (SFRH/BD/104038/2014).