Assessment of table olives' organoleptic defect intensities based on the potentiometric fingerprint recorded by an electronic tongue

Table olives are prone to the appearance of sensory defects that decrease their quality and in some cases result in olives unsuitable for consumption. The evaluation of the type and intensity of the sensory negative attributes of table olives is recommended by the International Olive Council, although not being legally required for commercialization. However, the accomplishment of this task requires the training and implementation of sensory panels according to strict directives, turning out in a time-consuming and expensive procedure that involves a degree of subjectivity. In this work, an electronic tongue is proposed as a taste sensor device for evaluating the intensity of sensory defects of table olives. The potentiometric signal profiles gathered allowed establishing multiple linear regression models, based on the most informative subsets of signals (from 24 to 29 recorded during the analysis of olive aqueous pastes and brine solutions) selected using a simulated annealing meta-heuristic algorithm. The models enabled the prediction of the median intensities (R2 ≥ 0.942 and RMSE ≤ 0.356, for leave-one-out or repeated K-fold cross-validation procedures) of butyric, musty, putrid, winey-vinegary, and zapateria negative sensations being, in general, the predicted intensities within the range of intensities perceived by the sensory panel. Indeed, based on the predicted mean intensities of the sensory defects, the electrochemical-chemometric approach developed could correctly classify 86.4% of the table olive samples according to their trade category based on a sensory panel evaluation and following the International Olive Council regulations (i.e., extra, 1st choice, 2nd choice, and olives that may not be sold as table olives). So, the satisfactory overall predictions achieved demonstrate that the electronic tongue could be a complementary tool for assessing table olive defects, reducing the effort of trained panelists and minimizing the risk of subjective evaluations.This work was financially supported by Project POCI-01-0145-FEDER-006984—Associate Laboratory LSRE-LCM, by Project UID/QUI/00616/2013 —CQ-VR, and UID/AGR/00690/ 2013—CIMO, all funded by Fundo Europeu de Desenvolvimento Regional (FEDER) through COMPETE2020—Programa Operacional Competitividade e Internacionalização (POCI) and by national funds through Fundação para a Ciência e a Tecnologia (FCT), 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).info:eu-repo/semantics/publishedVersio

Discrimination of olive oil by cultivar, geographical origin and quality using potentiometric electronic tongue fingerprints

Legal regulations are set for protecting claims regarding olive oil geographical denomination. When meteorological or agroecological factors similarly affect different regions, the origin identification is a challenging task. This study demonstrated the use of a potentiometric electronic tongue coupled with linear discriminant analysis to discriminate the geographical origin of monovarietal Tunisian olive oil produced from local cv Chemlali (Kairouan, Sidi Bouzid or Sfax regions) and cv Sahli (Kairouan, Mahdia or Sousse regions). The potentiometric fingerprints of 12 or eight lipid sensors (for Chemlali and Sahli, respectively), selected using a simulated annealing meta-heuristic algorithm, allowed the correct prediction (repeated K-fold cross-validation) of the geographic production region with sensitivities of 92 ± 7% (Chemlali) and 97 ± 8% (Sahli). It was also confirmed the electronic tongue capability to classify Tunisian olive oil according to olive cultivar or quality grade. The results indicated the possible use of potentiometric fingerprints as a promising innovative strategy for olive oil analysis allowing assessing geographical origin, olive cultivar and quality grade, which are key factors determining olive oil price and consumers preference.This work was financially supported by Project POCI-01–0145-FEDER-006984 - Associate Laboratory LSRE-LCM, Project UID/QUI/00616/2013 - CQ-VR, and UID/AGR/00690/2013 - CIMO all 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). Souheib Oueslati is also grateful for the support of the Tunisian Ministry of Agriculture.info:eu-repo/semantics/publishedVersio

Dairy products discrimination according to the milk type using an electrochemical multisensor device coupled with chemometric tools

This study shows the potential application of a potentiometric electronic tongue coupled with a lab-made DataLogger device for the classification of dairy products according to the type of milk used in their production, i.e., natural, fermented and UHT milk. The electronic tongue device merged a commercial pH electrode and 15 lipid/polymeric membranes, which were obtained by a drop-by-drop technique. The potentiometric signal profiles gathered from the 16 sensors, during the analysis of the 11 dairy products (with ten replicate samples), together with principal component analysis showed that dairy samples could be naturally grouped according to the three types of milk evaluated. To further investigate and verify this capability, a linear discriminant analysis together with a simulated annealing variable selection algorithm was also applied to the electrochemical data, which were randomly split into two datasets, one used for model training and internal-validation using a repeated K-fold cross-validation procedure (with 64% of the data); and the other for external validation purposes (containing the remaining 36% of the data). The multivariate supervised strategy used allowed establishing a classification model, based on the potentiometric information of four sensor lipid membranes, which enabled achieving a successful discrimination rate of 100% for both internal-and external-validation processes. The demonstrated versatility of the built electronic tongue for discriminating Dairy products according to the type of milk used in their production combined with its simplicity, low-cost and fast time analysis may envisage a possible future application in dairy industry