Combining classification and regression approaches for the quantification of highly overlapping capillary electrophoresis peaks by using evolutionary sigmoidal and product unit neural networks

Abstract

AbstractThis is a study of the potential of neural networks built by using different transfer functions (sigmoidal, product and sigmoidal–product units) designed by an evolutionary algorithm to quantify highly overlapping electrophoretic peaks. To test this approach, two aminoglycoside antibiotics, amikacin and paramomycin, were quantified from samples containing either only one component or mixtures of them though capillary zone electrophoresis (CZE) with laser‐induced fluorescence (LIF) detection. The three models assayed used as input data the four‐parameter Weibull curve associated with the profile of the electrophoretic peak and in some cases the class label for each sample estimated by cluster analysis. The combination of classification and regression approaches allowed the establishment of straightforward network topologies enabling the analytes to be quantified with great accuracy and precision. The best models for mixture samples were provided by product unit neural networks (PUNNs), 4:4:1 (14 weights) for both analytes, after discrimination by cluster analysis, allowing the analytes to be quantified with great accuracy: 8.2% for amikacin and 5.6% for paromomycin within the standard error of prediction for the generalization test, SEPG. For comparison, partial least square regression was also used for the resolution of these mixtures; it provided a minor accuracy: SEPG 11.8 and 15.7% for amikacin and paramomycin, respectively. The reduced dimensions of the neural networks models selected 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 other ANN models. Copyright © 2007 John Wiley & Sons, Ltd.