Increasing the working calibration range by means of artificial neural networks for the determination of cadmium by graphite furnace atomic absorption spectrometry.

Feed-forward artificial neural networks (ANNs), trained with the generalized delta rule, were evaluated for modeling the non-linear behavior of calibration curves and increasing the working range for the determination of cadmium by graphite furnace atomic absorption spectrometry (GFAAS). Selection of this analyte was made on the basis of its short linear range (up to 4.0mugl(-1)). Two-layer neural networks, comprising one node in the input layer (linear transfer function); a variable number of neurons in the hidden layer (sigmoid transfer functions), and a single neuron (linear transfer function) in the output layer were assessed for such a purpose. The (1:2:1) neural network was selected on the basis of its capacity to adequately model the working calibration curve in the range of study (0-22.0mugl(-1) Cd). The latter resulted in a nearly six fold increase in the working range. Cadmium was determined in the certified reference material "Trace Elements in Drinking Water" (High Purity Standards, Lot No. 490915) at four concentration levels (2.0, 4.0, 8.0 and 12.0mugl(-1) Cd), which were experimentally within and above the linear dynamic range (LDR). No significant differences (P<0.05) were found between the expected concentrations and the results obtained by means of the neural network. The proposed method was compared with the conventional "dilution" approach, and with fitting the working calibration curve by means of a second-order polynomial. Modeling by means of an ANN represents an alternative calibration technique, for its use helps in reducing sample manipulation (due to the extension of the working calibration range), and may provide higher accuracy of the determinations in the non-linear portion of the curve (as a result of the better fitness of the model).

Classification of Venezuelan spirituous beverages by means of discriminant analysis and artificial neural networks based on their Zn, Cu and Fe concentrations.

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.

Optimum phase-behavior formulation of surfactant/oil/water systems for the determination of chromium in heavy crude oil and in bitumen-in-water emulsion.

An "oil in water" formulation was optimized to determine chromium in heavy crude oil (HCO) and bitumen-in-water emulsion (Orimulsion-400(R)) samples by transversally heated electrothermal atomic absorption spectrometry (TH-ET AAS) using Zeeman effect background correction. The optimum proportion of the oil-water mixture ratio was 7:3 v/v (70 ml of oil as the internal phase) with a non-ionic surfactant concentration (Intan-100) in the emulsion of 0.2% w/w. Chromium was determined in different crude oil samples after dilution of the emulsions 1:9 v/v with a 0.2% w/w solution of surfactant in order to further reduce the viscosity from 100 to 1.6 cP and at the same time to bring the concentration of chromium within the working range of the ET AAS technique. The calibration graph was linear from 1.7 to 100 mug Cr l(-1). The sensitivity was of 0.0069 s l mug(-1), the characteristic mass (m(o)) was of 5.7 pg per 0.0044 s and the detection limit (3sigma) was of 0.52 mug l(-1). The relative standard deviation of the method, evaluated by replicate analyses of three crude oil samples varied in all cases between 1.5 and 2.6%. Recovery studies were performed on four Venezuelan crude oils, and the average chromium recovery values varied between 95.9-104.8, 90.6-107.6, 95.6-104.0 and 98.8-103.9% for the Cerro Negro, Crudo Hamaca and Boscán crude oils and for the Orimulsión(R)-400, respectively. The results obtained in this work for the Cerro Negro, Crudo Hamaca and Boscán crude oils and for the Orimulsión(R)-400 following the proposed procedure were of 0.448+/-0.008, 0.338+/-0.004 0.524+/-0.021 and 0.174+/-0.008 mg Cr l(-1), respectively, which were in good agreement with the values obtained by a tedious recommended standard procedure (respectively: 0.470+/-0.05, 0.335+/-0.080, 0.570+/-0.021 and 0.173+/-0.009 mg Cr l(-1)).