Comparison of multiple linear regression, partial least squares and artificial neural network for quantitative structure retention relationships of some polycyclic aromatic hydrocarbons

Abstract

Quantitative structural-retention relationships (QSRR) of retention phenomena of polycyclic aromatic hydrocarbons (PAHs) facilitate resolving complex mixtures of them by gas chromatography (GC). The structural descriptors of 38 PAH compounds were calculated. Stepwise variable selection was applied for selection of meaningful descriptors. MLR, PLS and ANN models were built with calibration compounds. The predictive ability of the models was evaluated on 6 PAHs, which were not used in training steps and also by leave-6-out cross-validation. The best prediction results were obtained by the MLR model. The difference in predictive ability of the ANN and PLS model was trivial.