Artificial neural networks in liquid chromatography: efficient and improved quantitative structure-retention relationship models.

Abstract

The application of the principal neural network architecture, namely the multilayer perceptron (MLP), has been developed for obtaining sufficient quantitative structure-retention relationships (QSRR) with high accuracy. The present study is an extension to the excellent study of Cserhati et al. [LC-GC Int., 11 (1998) 240] for the retention behavior of solutes based on their structure. To this end, a dataset of 25 substances as solutes to two different stationary phases (polyethylene-silica and polyethylene-alumina) were analyzed to their structural descriptors and related to their retention behavior as expressed by the logarithms of their capacity factors (log k'). The results were compared to those of Cserhati et al. who studied the same problem using as many as ten different equations based on multiple regression analysis. In the present study a series of new and improved algorithms other than the 'old-fashioned' and problematic steepest descent were examined for training the MLP networks. The proposed methods led to substantial gain in both the prediction ability and the computation speed of the resulting models. For the development and evaluation of the artificial neural network (ANN) systems the same (eight) descriptors proposed by Cserhati were used also in this study. Furthermore, the results were compared to those produced from classical linear multivariate regression such as partial least squares regression (PLS). Some of the proposed ANN models diminished the number of outliers, during their implementation to unseen data (solutes), to zero.