M. Julia Arcos

Genetic-algorithm-based wavelength selection in multicomponent spectrometric determinations by PLS: application on indomethacin and acemethacin mixture

A genetic algorithm is a suitable method for selecting wavelengths for PLS (partial least squares) calibration of mixtures with almost identical spectra without loss of prediction capacity. In the calibration of acemethacin and indomethacin, the proposed procedure eliminates the matrix effect due to the solvent which causes greater variability of the calibration spectra than that due to the difference in the concentration of the two drugs. For the calibration samples, the concentrations calculated using the wavelengths selected are, significantly, equal to those obtained with the full spectrum (significance level above 0.7 in the Student t-test for differences) and differ from the true ones in their average value — less than 1.8% relative error — for the four pH values used in the analysis.

Simultaneous determination of chromium(VI) and chromium(III) at trace levels by adsorptive stripping voltammetry

A new methodology was proposed for the speciation of chromium by differential pulse adsorptive stripping voltammetry (DPAdSV) using pyrocatechol violet (PCV) and N-(2-hydroxyethyl)ethylenediamine-N,N′,N′-triacetic acid (HEDTA) as complexing agents. In this procedure, a partial least squares (PLS) regression was used for the resolution of the strongly overlapping voltammetric signals from mixtures of Cr(III) and Cr(VI) in the presence of PCV and HEDTA. The relative error in absolute value was <6% when concentrations of several mixtures were calculated. The analysis of the possible effect of the presence of foreign ions in the solution was performed. The procedure was successfully applied to the speciation of chromium in different samples of natural water.

Genetic-algorithm-based potential selection in multivariant voltammetric determination of indomethacin and acemethacin by partial least squares

A procedure was proposed for the resolution of strongly overlapping voltammetric signals from mixtures of indomethacin and acemethacin. In this procedure a partial least squares regression (PLS) used the full voltammogram. The application of a genetic algorithm to select some of the predictor variables (potentials of the voltammogram) allows one to reduce by up to one tenth the number of experimental variables, but does not diminish the prediction of the PLS model constructed with these selected variables. The relative error in absolute value is less than 2% when concentrations of several mixtures are calculated.

Application of an Optimization Procedure in Adsorptive Stripping Voltammetry for the Determination of Chromium with Ammonium Pyrrolidine Dithiocarbamate

A procedure for the determination of chromium by differential pulse adsorptive stripping voltammetry (DPAdSV), using ammonium pyrrolidine dithiocarbamate (APDC) as complexing agent, has been optimized. The new method allows the selective determination of Cr(VI) in the presence of Cr(III) at pH equal to or below 3. The selection of the experimental conditions was made using experimental design methodology, and it was under these conditions where the calibrations were made, using a robust regression method which allows the elimination of anomalous points. The detection limit obtained was 1.09×10−9 M. The procedure was successfully applied to the determination of Cr(VI) and total chromium in different water samples.

Application of an Optimization Procedure for the Determination of Chromium in Various Water Types by Catalytic‐Adsorptive Stripping Voltammetry

A procedure for the determination of chromium by differential pulse catalytic-adsorptive stripping voltammetry (DPAdSV), using N-(2-hydroxyethyl) ethylenediamine-N, N ′, N ′-triacetic acid (HEDTA) as complexing agent, has been optimized. The selection of the experimental conditions was made using experimental design methodology. The calibrations were made under these conditions, using a robust regression method which allows for the elimination of anomalous points. The detection limit obtained was 1.84×10−10 mol dm−3. The procedure was successfully applied to the determination of chromium in different water types.

Optimization of the experimental parameters in the determination of copper(II) by differential-pulse anodic stripping voltammetry and evaluation of the characteristic detection curves

Differential-pulse anodic stripping voltammetry with a mercury microelectrode was used for the determination of copper with ethylenediaminetetraacetic acid as complexing agent. Interactions between analytical factors and their optimal levels were investigated using two factorial designs and the steepest ascent method. The calibration graph, calculated by least median squares, guarantees the linearity of the proposed calibration. Characteristic curves for the operating detection limit were established.

Optimization of digestion procedure for the determination of nickel in wine by differential-pulse adsorptive stripping voltammetry

A method for the determination of nickel in wine by differential-pulse adsorptive stripping voltammetry using dimethylglyoxime as complexing agent was optimised using experimental design methodology. In order to destroy organic matter, two different methods of treating the sample were considered. The UV irradiation method was selected and optimised for a smaller degree of uncertainty. The recovery factor obtained with five replicates was 1.02 ± 0.05.

Multivariate detection capability using a neural classifier for nonselective signals

Abstract A new methodology is proposed based on a neural network to determine the detection capability of an analytical procedure, in complex matrices, with the evaluation of the probability of false detection, α , and false nondetection, β , according to the ISO norms. This methodology is designed for first or greater order signals for which there is currently no procedure with these characteristics, which makes it difficult to use these signals in analytical procedures standardized according to the ISO norm. The procedure consists of: (i) an experimental design suited to the increase in analyte to be detected from a threshold level; (ii) a homogenisation of the multivariate signals by a Piecewise Direct Standardization (PDS) transformation; (iii) the training of a neural network with stochastic learning, Genetic Inside Neural Network (GINN), which optimises α and β directly. The procedure was applied to the polarographic determination of Tl(I)/Pb(II) mixtures and indomethacin/tenoxicam mixtures. In the first case one can assure the detection of 1 mM (threshold: 12 mM) with α and β less than 5% for both metals. While for tenoxicam it is possible to detect less than 10% of 12 mM (threshold) with α β α and β less than 5%.