RESUMO
A continuous flow system for the determination of lead in home made spirituous beverages was developed. The determination was based on the formation of a neutral chelate of the element with ammonium pyrrolidine dithiocarbamate, its adsorption onto a minicolumn packed with sodium faujasite type Y synthetic zeolite, followed by elution with methyl isobutyl ketone and determination by flame atomic absorption spectrometry. Ethanol and copper interfere strongly in the determination and therefore, must be separated prior to the analysis. Copper is removed by precipitation with rubeanic acid, while ethanol is eliminated by rotaevaporation. Sample solutions containing Pb(2+) in the concentration range from 5 to 120mugl(-1) at pH 2.5 could be analyzed, by using a preconcentration time of 3min. Preconcentration factors from 80 to 140 were achieved for a sample volume of 6ml and the detection limit varied from 1.4 to 3.5mugl(-1), depending on the matrix composition. The relative standard deviations for 60mugl(-1) Pb was 3.2% (n = 10) and the recovery of spikes (20, 40, 60 and 80mugl(-1)) added to the samples was estimated within 92-105% range, suggesting that lead can be quantitatively determined in such samples. Determining lead in several samples by an alternative technique further checked the accuracy. Finally, the concentrations of Pb(2+) determined in 28 samples of Venezuelan spirituous beverages were in 12.6-370.0mugl(-1) range, depending on the fermenting material based on different mixtures of agave, raw sugar cane and white sugar.
RESUMO
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 Falcón 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.