Currently several different approaches are used for speed-up and cost reduction for new method development in reversed-phase high-performance liquid chromatography. During this research, application of a solvatic retention model of reversed-phase high-performance liquid chromatography was studied to predict the retention of phenylisothiocyanate derivatives of 25 natural amino acids, working with different stationary phases. The gradient elution mode was used, with methanol and acetonitrile as the aqueous mobile phases. Retention factors were calculated from the molecular parameters of the structures of the analytes and stationary and mobile phase properties. Such step-by-step methods, which include the first-guess prediction of initial conditions from structural formulae and fine tuning parameters of the retention model using data from successive runs, can save time and consequently will reduce the cost of method development and optimization.
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