LAUSR

The application of a voltammetric electronic tongue (ET) towards the classification and authentication of vinegar is reported. Vinegar samples of different varieties were analysed with a three-sensor array, without performing any sample pre-treatment, but only an electrochemical cleaning stage between sample measurements to avoid fouling onto the electrode surfaces. Next, the use of discrete cosine transform (DCT) for the compression and reduction of signal complexity in voltammetric measurements was explored, and the number of coefficients was optimized through its inverse transform. Finally, the obtained coefficients were analysed by principal component analysis (PCA) to attempt the discrimination of the different vinegars and by linear discriminant analysis (LDA) to build a model that allows its categorization. Satisfactory results were obtained overall, with a classification rate of 100% for the external test subset (n = 15).