Qualitative and quantitative aspects of the application of genetic algorithm-based variable selection in polarography and stripping voltammetry Original Research Article

A genetic algorithm (GA) is successfully applied as a variable selection method in the multivariate analysis with partial least squares (PLS) regression of several polarographic and stripping voltammetric data sets, where different interferences are present (coupled reactions, formation of intermetallic compounds, overlapping signals and matrix effect). In most cases, the results corresponding to this variable selection method are better than those obtained when all the variables are considered. Such is the case in the determination of benzaldehyde, where a dimerization reaction occurs simultaneously to the electrochemical reactions. In general, an improvement in the precision is achieved for the test samples by using the GA. On the other hand, the GA provides valuable qualitative information that, in every case, provides a significant tool to detect and understand the chemical phenomena related to each analysis.