Artificial neural networks aided deconvolving overlapped peaks in chromatograms

A novel method for deconvolving overlapped peaks in chromatograms is proposed. The basic idea of this method consists of finding a set of parameters which characterize the shape of the overlapped peaks and using a multi-layered perceptron network for quantitatively correlating the parameters with the percentage area of an individual peak. The proposed method performs very well with high accuracy and less computing time compared to other conventional methods.