This work reports the application of a voltammetric electronic tongue system (ET) made from an array of modified graphite-epoxy composites plus a gold microelectrode in the qualitative and quantitative analysis of polyphenols found in wine. Wine samples were analyzed using cyclic voltammetry without any sample pretreatment. The obtained responses were preprocessed employing discrete wavelet transform (DWT) in order to compress and extract significant features from the voltammetric signals, and the obtained approximation coefficients fed a multivariate calibration method (artificial neural network-ANN-or partial least squares-PLS-) which accomplished the quantification of total polyphenol content. External test subset samples results were compared with the ones obtained with the Folin-Ciocalteu (FC) method and UV absorbance polyphenol index (I(280)) as reference values, with highly significant correlation coefficients of 0.979 and 0.963 in the range from 50 to 2400 mg L(-1) gallic acid equivalents, respectively. In a separate experiment, qualitative discrimination of different polyphenols found in wine was also assessed by principal component analysis (PCA).
A multiparametric system able to classify red and white wines according to the grape varieties and for analysing some specific parameters is presented. The system, known as hybrid electronic tongue, consists of an array of electrochemical microsensors and a colorimetric optofluidic system. The array of electrochemical sensors is composed of six ISFETs based sensors, a conductivity sensor, a redox potential sensor and two amperometric electrodes, an Au microelectrode and a microelectrode for sensing electrochemical oxygen demand. The optofluidic system is entirely fabricated in polymer technology and comprises a hollow structure, air mirrors, microlenses and self-alignment structures. The data obtained from these sensors has been treated with multivariate advanced tools; Principal Component Analysis (PCA), for the patterning recognition and classification of wine samples, and Partial-Least Squares (PLS) regression, for quantification of several chemical and optical parameters of interest in wine quality. The results have demonstrated the utility of this system for distinguishing the samples according to the grape variety and year vintage and for quantifying several sample parameters of interest in wine quality control.
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