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Separation optimization in reversed-phase liquid chromatography by using alkanol additives in the mobile phase: application to amino acids.


PMID 21872084

Abstract

In an effort to enhance complex mixture separations by using small amounts of a homologous series of alkanols as additives in the mobile phases, it was proposed an optimization algorithm based on a sixth-parameter retention model. This model considers simultaneously the contents of the main organic modifier and of the alkanol additive in the mobile phase as well as of the number of alkyl chain of the additive. This model is in fact a modification of a previously one derived in a recently published paper for the retention description of a mixture of purely hydrophobic alkylbenzenes under isocratic conditions with mobile phases containing alkanol additives. The effectiveness of the new retention model as well as the optimization algorithm was successfully applied to the separation of ten o-phthalaldehyde (OPA) derivatives of amino acids. Indeed, the new retention model exhibited an excellent prediction performance since the obtained overall predictive error between calculated and experimental times was only 2.8% for all isocratic runs by using a variety of mobile phase compositions containing any alkanol homologue even different than those used in the starting/fitting experiments. Moreover, a perfect resolution of the above amino acid mixture was achieved within only 7.4 min in the chromatogram recorded using the optimal mobile phase determined by means of the simple optimization algorithm proposed in this study.