This work demonstrates the use of a voltammetric electronic tongue formed by five modified graphite-epoxy electrodes in the qualitative and quantitative analysis of cava wines. The different samples were analyzed using cyclic voltammetry without any sample pretreatment. Recorded data were evaluated by Principal Component Analysis and Discrete Wavelet Transform in order to compress and extract significant features from the voltammetric signals. The preprocessed information was evaluated by an Artificial Neural Network that accomplishes the qualitative classification. Moreover, a preliminary study related to the quantification of sugar amount present was assessed by Second-Order Standard Addition Method.

a b s t r a c tThis paper reports the use of a hybrid electronic tongue based on data fusion of two different sensor families, applied in the recognition of beer types. Six modified graphite-epoxy voltammetric sensors plus 15 potentiometric sensors formed the sensor array. The different samples were analyzed using cyclic voltammetry and direct potentiometry without any sample pretreatment in both cases. The sensor array coupled with feature extraction and pattern recognition methods, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), was trained to classify the data clusters related to different beer varieties. PCA was used to visualize the different categories of taste profiles, while LDA with leave-one-out cross-validation approach permitted the qualitative classification. The aim of this work is to improve performance of existing electronic tongue systems by exploiting the new approach of data fusion of different sensor types.

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