Abstract

Copper, zinc and iron concentrations were determined in ''aguardiente de Cocuy de Penca'' (Cocuy de Penca firewater), a spirituous beverage very popular in the North-Western region of Venezuela, by flame atomic absorption spectrometry (FAAS). These elements were selected for their presence can be traced to the (illegal) manufacturing process of the aforementioned beverages. Linear and quadratic discriminant analysis (QDA), and artificial neural networks (ANNs) trained with the backpropagation algorithm were employed for estimating if such beverages can be distinguished based on the concentrations of these elements in the final product, and whether it is possible to assess the geographic location of the manufacturers (Lara or Falco´n states) and the presence or absence of sugar in the end product. A linear discriminant analysis (LDA) performed poorly, overall estimation and prediction rates being 51.7% and 50.0%, respectively. A QDA showed a slightly better overall performance, yet unsatisfactory (estimation: 79.2%, prediction: 72.5%). Various ANNs, comprising a linear function (L) in the input layer, a sigmoid function (S) in the hidden layer(s) and a hyperbolic tangent function (T) in the output layer, were evaluated. Of the networks studied, the (3L:5S:7S:4T) gave the highest estimation (overall: 96.5%) and prediction rates (overall: 97.0%), demonstrating the superb performance of ANNs for classification purposes.

Artículo publicado en: Talanta 60 (2003) 1259-1267