Discrimination of Venezuelan spirituous beverages by a trace element-radial basis neural network approach

Radial basis neural networks (RBNNs) were developed and evaluated for discrimination of specimens of ‘aguardiente de Cocuy’, a spirituous beverage produced in the northwestern region of Venezuela. The beverage is distilled from the must of Agave cocui Trelease in an artisanship fashion with little quality control. Forty specimens, with known concentrations of copper, iron, and zinc, were used in this study. The specimens were previously collected in various locations around Sucre Municipality (Falcón State) and Urdaneta Municipality (Lara State). The normalized concentrations of these elements served as indirect descriptors of origin (input data). They were presented to the neural networks through 1–3 input nodes in seven different combinations. In addition, two categories (two collection sites) and four categories (two collection sites + two manufacturing conditions) were designated as output data, in order to assess the impact of such selection on the discrimination performance. The overall performance of the four-category RBNNs was as follows (the input data is indicated in parentheses): (Cu–Fe) > (Cu–Zn) > (Cu) > (Zn) > (Fe–Zn) > (Cu–Fe–Zn) > (Fe). In this case, the highest percentage of correct hits was 82.5%. For the two-category RBNNs, the performance decreased as indicated below: (Cu) > (Cu–Fe) > (Cu–Zn) > (Fe–Zn) > (Zn) ≈ (Cu–Fe–Zn) > (Fe). The reduction in the number of categories led to an increase in the discrimination performance of all the RBNNs, the best of which was 90.0%. The possibility of discriminating specimens of ‘aguardiente de Cocuy’ with such an accuracy, based on a single-element determination, is particularly attractive as it would result in a reduction of analysis’ costs and laboratory's response time.