Elucidation of the retention mechanism on a reverse-phase cyano column by modeling

Partial least squares and quantitative structure-retention relationship models have been used mainly to explain and then to predict the retention mechanism on a cyanopropyl high-performance liquid chromatography column. Developing and applying the models involves studying the chromatographic behavior of 100 probes. Characterization of the probes took place under optimized isocratic conditions at variable proportions of two mobile phase mixtures. Retention time was correlated with numerous physicochemical properties and structural features of the probes. The goodness-of-fit for both models was estimated by the coefficient of multiple determinations, while the prediction of a test set was achieved by the root mean square error of prediction. The contribution of the descriptors in partial least squares is confirmed by the information derived from the variable importance in the projection and loadings plots, while a quantitative structure-retention relationship reflects the behavior model. In both cases, the descriptors determining the retention mechanism are lipophilicity, solubility in water, molecular volume and the presence of -COOH and/or condensed rings. Such techniques are proven useful tools for visualizing, exploring, and modeling the complex interactions between solutes and the mobile and stationary phase while at the same time this information can be quantified.