Efficiency of Solvatic Sorption Model for Predicting the Retention in Multi-step Gradient RP-LC with Different Stationary Phases

Abstract

Gradient elution is widely applied in analytical chromatography to reduce separation time and improve selectivity. However, the development and optimization of high-performance liquid chromatography (HPLC) gradient methods is arduous and time-consuming. In this paper, we demonstrate a solvatic sorption model to predict the retention time for phenylisothiocyanate derivatives of amino acids in a multi-step gradient reversed-phase HPLC. This model uses zero approximation level predictions. Rather, we use structural formulae and column and mobile phase properties as a “first guess” to develop the HPLC method before further optimization and prediction of the best multi-step gradient profile. The gradient elution mode with mobile phases modified with methanol and acetonitrile was used and verified the efficiency of different stationary phases. This approach provides good predictions of retention time values achieved after the first approximation step—this uses the data from only one experimental run.