A new voltammetric electronic tongue formed with graphite-epoxy composite electrodes which were modified with metal-oxide nanoparticles is presented for the quantification of tryptophan, tyrosine and cysteine aminoacid mixtures. The signals were obtained by cyclic voltammetry, and data was processed using two different chemometric techniques, artificial neural networks and partial least squares regression, for comparison purposes. Before performing artificial neural networks data was compressed by fast Fourier transform or discrete wavelet transform. The best results were attained using artificial neural networks with previous fast Fourier transform compression of the data with a normalized root-mean-square error of 0.032 (n=15) for the external test subset. The present method shows results comparable to other similar approaches, but with a much easier sampling process for the training set and new electrode modifiers to form the voltammetric sensors.
