Artificial neural networks based on principal component analysis input selection for quantification in overlapped capillary electrophoresis peaks

The application of three different kinds of artificial neural networks (ANN) based on principal component analysis (PCA) input selection for quantification of overlapped peaks in micellar electrokinetic capillary chromatography (MECC) is investigated. In the case of overlapped peaks, ANN based on PCA input selection were proved to be a promising approach for quantification of the corresponding components. Both the spectra and the electrophoretograms of the unseparated analytes were used as the multivariate input data. The two kinds of data were both suitable for quantification of overlapped peaks by ANN based on PCA input selection. In the study, it was also shown that the input selection based on PCA for the three kinds of ANN could improve the precision of quantification of the corresponding components in both completely and partially overlapped peaks to some extent.