a b s t r a c tOlive oils may be commercialized as intense, medium or light, according to the intensity perception of fruitiness, bitterness and pungency attributes, assessed by a sensory panel. In this work, the capability of an electronic tongue to correctly classify olive oils according to the sensory intensity perception levels was evaluated. Cross-sensitivity and non-specific lipid polymeric membranes were used as sensors. The sensor device was firstly tested using quinine monohydrochloride standard solutions. Mean sensitivities of 14 7 2 to 257 6 mV/decade, depending on the type of plasticizer used in the lipid membranes, were obtained showing the device capability for evaluating bitterness. Then, linear discriminant models based on sub-sets of sensors, selected by a meta-heuristic simulated annealing algorithm, were established enabling to correctly classify 91% of olive oils according to their intensity sensory grade (leave-one-out cross-validation procedure). This capability was further evaluated using a repeated K-fold cross-validation procedure, showing that the electronic tongue allowed an average correct classification of 80% of the olive oils used for internal-validation. So, the electronic tongue can be seen as a taste sensor, allowing differentiating olive oils with different sensory intensities, and could be used as a preliminary, complementary and practical tool for panelists during olive oil sensory analysis.

A B S T R A C T Table olives are highly appreciated and consumed worldwide. Different aspects are used for trade category classification being the sensory assessment of negative defects present in the olives and brines one of the most important. The trade category quality classification must follow the International Olive Council directives, requiring the organoleptic assessment of defects by a trained sensory panel. However, the training process is a hard, complex and sometimes subjective task, being the low number of samples that can be evaluated per day a major drawback considering the real needs of the olive industry. In this context, the development of electronic tongues as taste sensors for defects' sensory evaluation is of utmost relevance. So, an electronic tongue was used for table olives classification according to the presence and intensity of negative defects. Linear discrimination models were established based on sub-sets of sensor signals selected by a simulated annealing algorithm. The predictive potential of the novel approach was first demonstrated for standard solutions of chemical compounds that mimic butyric, putrid and zapateria defects (≥93% for cross-validation procedures). Then its applicability was verified; using reference table olives/brine solutions samples identified with a single intense negative attribute, namely butyric, musty, putrid, zapateria or winey-vinegary defects (≥93% cross-validation procedures). Finally, the E-tongue coupled with the same chemometric approach was applied to classify table olive samples according to the trade commercial categories (extra, 1 st choice, 2 nd choice and unsuitable for consumption) and an additional quality category (extra free of defects), established based on sensory analysis data. Despite the heterogeneity of the samples studied and number of different sensory defects perceived, the predictive linear discriminant model established showed sensitivities greater than 86%. So, the overall performance achieved showed that the electrochemical device could be used as a taste sensor for table olives organoleptic trade successful classification, allowing a preliminary quality assessment, which could facilitate, in the future, the complex task of sensory panelists.

Monitoring olive oils oxidative stability and quality parameters (free acidity, peroxide values, K 232 and K 270 extinction coefficients) is needed to guarantee that, during storage, their levels remain within the legal thresholds enabling their commercialization as high-value extra-virgin olive oils. Physicochemical levels are assessed using time-consuming routine analytical reference techniques. In this work, the feasibility of a novel approach that merges an electronic tongue and chemometric tools, for monitoring extra-virgin olive oils' quality along one year of storage at dark or exposed to light is discussed. The results confirmed that physicochemical parameters varied with the storage lighting conditions and more significantly with time. Also, multiple linear regression models, using subsets of 22e28 sensors selected with a meta-heuristic simulated annealing algorithm, allow evaluating the storage time-evolution of olive oils' peroxide values, extinction coefficients and oxidative stabilities with satisfactory accuracy (R 2 ! 0.98 and ! 0.96, for leave-one-out and repeated K-fold cross-validation procedures, respectively). The capability of monitoring, in a single electrochemical assay, legal required quality parameters of olive oils, decreases considerable the analysis time and cost, allowing checking the compliance of extra-virgin olive oil quality with labeling. So, the use of electronic tongues for extra-virgin olive oil shelf-life assessment could be envisaged.

a b s t r a c tThe intensities of the gustatory attributes of table olives is one of the sensory set of parameters evaluated by trained sensory panels accordingly to the recommendations of the International Olive Council. However this is an expensive and time-consuming process that only allows the evaluation of a limited number of samples per day. So, an electronic tongue coupled with multivariate statistical tools, is proposed for assessing the median intensities of acid, bitter and salty tastes perceived in table olives. The results showed that the device, coupled with linear discriminant analysis, could be used as a taste sensor, allowing classifying aqueous standard solutions according to the three basic tastes (repeated K-fold cross-validation: 98% ± 3% of correct classifications) based on the electrochemical signals of 5 sensors. It was demonstrated that the taste sensor with multiple linear regression models, enabled quantifying the median intensities of the three basic tastes (repeated K-fold cross-validation: R 2 ! 0.96 ± 0.04) perceived in table olives by a trained sensory panel, based on the potentiometric fingerprints (21e25 signal profiles) of aqueous olive pastes and brines. The overall satisfactory results showed the electronic tongue potential to assess the intensities of gustatory attributes of table olives, formerly only achievable by sensory panels.

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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.

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A B S T R A C TThe capability of perceiving olive oils sensory defects and intensities plays a key role on olive oils quality grade classification since olive oils can only be classified as extra-virgin if no defect can be perceived by a human trained sensory panel. Otherwise, olive oils may be classified as virgin or lampante depending on the median intensity of the defect predominantly perceived and on the physicochemical levels. However, sensory analysis is time-consuming and requires an official sensory panel, which can only evaluate a low number of samples per day. In this work, the potential use of an electronic tongue as a taste sensor device to identify the defect predominantly perceived in olive oils was evaluated. The potentiometric profiles recorded showed that intraand inter-day signal drifts could be neglected (i.e., relative standard deviations lower than 25%), being not statistically significant the effect of the analysis day on the overall recorded E-tongue sensor fingerprints (Pvalue = 0.5715, for multivariate analysis of variance using Pillai's trace test), which significantly differ according to the olive oils' sensory defect (P-value = 0.0084, for multivariate analysis of variance using Pillai's trace test). Thus, a linear discriminant model based on 19 potentiometric signal sensors, selected by the simulated annealing algorithm, could be established to correctly predict the olive oil main sensory defect (fusty, rancid, wet-wood or winey-vinegary) with average sensitivity of 75 ± 3% and specificity of 73 ± 4% (repeated K-fold cross-validation variant: 4 folds×10 repeats). Similarly, a linear discriminant model, based on 24 selected sensors, correctly classified 92 ± 3% of the olive oils as virgin or lampante, being an average specificity of 93 ± 3% achieved. The overall satisfactory predictive performances strengthen the feasibility of the developed taste sensor device as a complementary methodology for olive oils' defects analysis and subsequent quality grade classification. Furthermore, the capability of identifying the type of sensory defect of an olive oil may allow establishing helpful insights regarding bad practices of olives or olive oils production, harvesting, transport and storage.