Monitoring the aging of beers using a bioelectronic tongue

This paper deals with the implementation and the application of a bioelectronic tongue including three enzymatic biosensors based on tyrosinase and phthalocyanines as electron mediators, to evaluate the changes that occur during the aging of beers. For this purpose, alcoholic and non alcoholic beers, packaged in can and bottle, have been analyzed using cyclic voltammetry. The electrochemical signals showed significant changes during the aging process. The features extracted from the cyclic voltammograms have been used to perform Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Data have revealed a clear discrimination among the beer classes in the aging process and the results were confirmed by Probabilistic Neural Networks (PNN) with Radial Basis Functions (RBF) and FeedForward Networks with Backpropagation (BP) learning method. The bioelectronic tongue has demonstrated a good capability to discriminate and classify the beer types satisfactorily in such a way, for all beer treatments, full classification accuracy was found.