Improving the quantification of highly overlapping chromatographic peaks by using product unit neural networks modeled by an evolutionary algorithm

Abstract

This work investigates the ability of multiplicative (on the bAsís 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.