Solvent-Dependent Regression Equations for the Prediction of Retention in Planar Chromatography

Abstract

Both first- and second-order regression models are presented that relate retention, as the R f of individual solutes, the log k' of individual solutes, or the average R f of a mixture of solutes, to the properties of the weak solvent in each of a series of 25 binary mobile phases consisting of a specified concentration of ethyl acetate as a common strong solvent. The stepwise procedure is used for constructing these models, which are for either simulated or experimental separations on silica gel. Similar regression models are used to predict separation quality, defined by a suitable metric. A comparison of the forward and backward stepwise procedures finds that the former is the more reliable method for constructing these models. The solutes are either steroids or the p-nitrobenzyl esters of dansyl amino acids, and the solvent descriptors are density, dipole moment, molar volume, polarizability, saturated surface area, and unsaturated surface area. The quality of regression fits obtained with models using computed dipole moment is comparable to that obtained with models using experimental (literature) dipole moment. Both nonstandardized and standardized regression models are presented. The relative contribution of each descriptor to the variability in retention may be estimated from the latter models. A set of three descriptors-dipole moment, polarizability, and saturated surface area-predicts R f for each of the amino acid derivatives at an ethyl acetate mole fraction of 0.30. A set of two descriptors-dipole moment and saturated surface area-predicts log k' for each of these compounds at an ethyl acetate mole fraction of 0.20. Such concordance in descriptors is not found in models predicting retention of individual steroids.