Multivariate characterization of solvent strength and solvent selectivity in reversed-phase high-performance liquid chromatography

Abstract Principal component analysis was used to determine the dimensionality and structure of three data sets consisting of the capacity factors of eleven to twenty different solutes measured in nine different mobile phase compositions consisting of water and methanol and/or acetonitrile on three reversed-phase columns. Principal component analysis showed that two principal components could account for the total variance in the data and that the percentage variance explained by the first principal component (about 80–95%) was much greater than the percentage explained by the second principal component, but that the percentage depended strongly on the choice of solutes for the sample. The first principal component could be associated with solvent strength and solvent strength selectivity and the second principal component with modifier selectivity. Solutes that showed strong modifier selectivity could be distinguished from solutes that have almost zero modifier selectivity, which could be useful for the definition of an empirical solvent strength scale.