Predicting retention in reverse-phase liquid chromatography at different mobile phase compositions and temperatures by using the solvation parameter model

Abstract

The prediction capability of the solvation parameter model in reverse-phase liquid chromatography at different methanol-water mobile phase compositions and temperatures was investigated. By using a carefully selected set of solutes, the training set, linear relationships were established through regression equations between the logarithm of the solute retention factor, logk, and different solute parameters. The coefficients obtained in the regressions were used to create a general retention model able to predict retention in an octadecylsilica stationary phase at any temperature and methanol-water composition. The validity of the model was evaluated by using a different set (the test set) of 30 solutes of very diverse chemical nature. Predictions of logk values were obtained at two different combinations of temperature and mobile phase composition by using two different procedures: (i) by calculating the coefficients through a mathematical linear relationship in which the mobile phase composition and temperature are involved; (ii) by using a general equation, obtained by considering the previous results, in which only the experimental values of temperature and mobile phase composition are required. Predicted logk values were critically compared with the experimental values. Excellent results were obtained considering the diversity of the test set.