Classification of wine samples by means of artificial neural networks and discrimination analytical methods

Abstract

 The three-layer artificial neural network (ANN) model with back-propagation (BP) of error was used to classify wine samples in six different regions based on the measurements of trace amounts of B, V, Mn, Zn, Fe, Al, Cu, Sr, Ba, Rb, Na, P, Ca, Mg,  K using an inductively coupled plasma optical emission spectrometer (ICP-OES). The ANN architecture and parameters were optimized. The results obtained with ANN were compared with those obtained by cluster analysis, principal component analysis, the Bayes discrimination method and the Fisher discrimination method. A satisfactory prediction result (100%) by an artificial neural network using the jackknife leave-one-out procedure was obtained for the classification of wine samples containing six categories.