Development of comprehensive descriptors for multiple linear regression and artificial neural network modeling of retention behaviors of a variety of compounds on different stationary phases

A new series of six comprehensive descriptors that represent different features of the gas–liquid partition coefficient, KL, for commonly used stationary phases is developed. These descriptors can be considered as counterparts of the parameters in the Abraham solvatochromic model of solution. A separate multiple linear regression (MLR) model was developed by using the six descriptors for each stationary phase of poly(ethylene glycol adipate) (EGAD), N,N,N′,N′-tetrakis(2-hydroxypropyl) ethylenediamine (THPED), poly(ethylene glycol) (Ucon 50 HB 660) (U50HB), di(2-ethylhexyl)phosphoric acid (DEHPA) and tetra-n-butylammonium N,N-(bis-2-hydroxylethyl)-2-aminoethanesulfonate (QBES). The results obtained using these models are in good agreement with the experiment and with the results of the empirical model based on the solvatochromic theory. A 6-6-5 neural network was developed using the descriptors appearing in the MLR models as inputs. Comparison of the mean square errors (MSEs) shows the superiority of the artificial neural network (ANN) over that of the MLR. This indicates that the retention behavior of the molecules on different columns show some nonlinear characteristics. The experimental solvatochromic parameters proposed by Abraham can be replaced by the calculated descriptors in this work.