Quantitative chiral analysis in capillary electrophoresis from unresolved peaks using derivative electropherograms,experimental design,and artificial neural networks

Quantitative capillary electrophoretic analysis of chiral compounds might be difficult or even impossible when baseline separation is not reached. In this work, the use of n-th derivative of the electropherogram was studied and examined on model and experimental data. The electropherograms should be first smoothed using Savitzky-Golay method and the quantitative analysis is then possible using either a graphical method or multivariate calibration applying a combination of experimental design (ED) and artificial neural networks (ANNs). The best results were obtained for the first derivative, higher derivatives are not suitable because of noise accumulation. The method was applied to real experimental data to quantify chiral amino acids from unresolved peaks, but it is applicable for quantitative analysis of any other chiral analytes from poorly resolved peaks. Precision of analysis from partially resolved peaks reached was about +/- 3.2% relative standard deviation.