Improving the Quantification of Highly Overlapping Chromatographic Peaks by Using Product Unit Neural Networks Modeled by an Evolutionary Algorithm

This work investigates the ability of multiplicative (on the basis of product units) and sigmoidal neural models built by an evolutionary algorithm to quantify highly overlapping chromatographic peaks. To test this approach, two N-methylcarbamate pesticides, carbofuran and propoxur, were quantified using a classic peroxyoxalate chemiluminescence reaction as a detection system for chromatographic analysis. The four-parameter Weibull curve associated with the profile of the chromatographic peak estimated by the Levenberg-Marquardt method was used as input data for both models. Straightforward network topologies (one output) allowed the analytes to be quantified with great accuracy and precision. Product unit neural networks provided better information ability, smaller network architectures, and more robust models (smaller standard deviation). The reduced dimensions of the selected models enabled the derivation of simple quantification equations to transform the input variables into the output variable. These equations can be more easily interpreted from a chemical point of view than those provided by sigmoidal neural networks, and the effect of both analytes on the characteristics of chromatographic bands, namely profile, dispersion, peak height, and residence time, can be readily established.