Assessment of chromatographic peak purity by means of artificial neural networks

Abstract

An improved chemometric approach is proposed for assessing chromatographic peak purity by means of artificial neural networks. A non-linear transformation function with a back-propagation algorithm was used to describe and predict the chromatographic data. The mann-Whitney U-test was used for the concluding the purity of the chromatographic peak. Simulation data and practical analytical data for both pure and mixture samples were analysed with satisfactory results. A prior knowledge of the impurity and the related compound is unecessary when a slight difference between their chromatogram and spectrum exists. The performance on simulated data sets by this approach was compared with the results from principal component analysis.