Evaluation of the Effect of Extracted Time Conditions on the Phenolic Content of Olive Pastes from cv. Arbequina and Discrimination Using a Lab-Made Potentiometric Electronic Tongue
Academic ArticleConference Paper
The authors are grateful to the Foundation for Science and Technology (FCT,
Portugal) for financial support by national funds FCT/MCTES to CIMO (UIDB/00690/2020), to
CEB (UIDB/04469/2020), to REQUIMTE-LAQV (UIDB/50006/2020) and to BioTecNorte operation
(NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the
scope of Norte2020—Programa Operacional Regional do Norte. Ítala Marx acknowledges the Ph.D.
research grant (SFRH/BD/137283/2018) provided by FCT. Nuno Rodrigues thanks the National
funding by FCT-Foundation for Science and Technology, P.I., through the institutional scientific
employment program contract.
The present study investigated the effect of malaxation times (Mt) (0, 15, 30, 45 and 60 min), during the industrial extraction of cv. Arbequina oils at 25 °C on total phenolic content of olive pastes. Additionally, the possibility of applying a lab-made potentiometric electronic tongue (E-tongue), comprising 40 lipid/polymer sensor membranes with cross sensitivity, to discriminate the olive pastes according to the Mt, was evaluated. The results pointed out that the olive pastes’ total phenolic contents significantly decreased (p-value < 0.001, one-way ANOVA) with the increase of the Mt (from 2.21 ± 0.02 to 1.99 ± 0.03 g gallic acid equivalents/kg olive paste), there being observed a linear decreasing trend (R-Pearson = −0.910). These findings may be tentatively attributed to the migration of the phenolic compounds from the olive pastes to the extracted oil and water phases, during the malaxation process. Finally, the E-tongue signals, acquired during the analysis of the olive pastes’ methanolic extracts (methanol:water, 80:20 v/v), together with a linear discriminant analysis (LDA), coupled with a simulated annealing (SA) algorithm, allowed us to establish a successful classification model. The E-tongue-LDA-SA model, based on 11 selected non-redundant sensors, allowed us to correctly discriminate all the studied olive pastes according to the Mt (sensitivities of 100% for training and leave-one-out cross-validation). The satisfactory performance of the E-tongue could be tentatively explained by the known capability of lipid/polymeric sensor membranes to interact with phenolic compounds, through electrostatic interactions and/or hydrogen bonds, which total content depended on the Mt.
