Investigation of Linear and Nonlinear Chemometrics Methods in Modeling of Retention Time of Phenol Derivatives Based on Molecular Descriptors

Abstract

Quantitative Structure-Retention Relationship (QSRR) studies were performed for predicting the gas chromatographic retention times of phenol derivatives on Rtx-200 stationary phase with medium polarity. First, a number of descriptors were calculated using Hyperchem and Mopac softwares. Partial least squares (PLS) and multiple linear regressions (MLR) were used as linear modeling methods. Then, selected descriptors using the MLR model were used as inputs for artificial neural networks with different weight update functions including the Levenberg-Marquardt back propagation algorithm (LM-ANN), the resilient back propagation algorithm (RP-ANN), and the variable learning rate algorithm (GDX-ANN). The stability and the validity of the models were tested by cross-validation, Y-randomization, and external validation set. Moreover, the mean effect of the descriptors indicates that molecular weight (MW) is the most important factor affecting the retention behavior of molecules.