Improvements in the predictive accuracy of the hydrophobic subtraction model of reversed-phase selectivity

Abstract

The hydrophobic subtraction model (HSM) for characterizing the selectivity of reversed-phase liquid chromatography (LC) columns has been used extensively by the LC community since it was first developed in 2002. Continuing interest in the model is due in part to the large, publicly available set of column descriptors that has been assembled over the past 18 years. In the work described in this report, we sought to refine the HSM with the goal of improving the predictive accuracy of the model without compromising its physico-chemical interpretability. The approach taken here has the following facets. A set of retention measurements for 635 columns and the 16 probe solutes used to characterize new columns using the HSM was assembled. Principal components analysis (PCA) was used as a guide for the development of a refined version of the HSM. Several outlying columns (84) were eliminated from the analysis because they were either inconsistent with the PCA model or were outliers from the original HSM model. With the retention dataset for the 16 probe solutes on the remaining 551 columns, we determined that a six-component model is the most sophisticated form of the model that can be used without overfitting the data. In our refined version of the HSM, the S*σ term has been removed. Two new terms have been added, which more accurately account for the molecular volume of the solute (Vv), and the solute dipolarity (Dd), and the remaining terms have been adjusted to accommodate these changes. The refined model described here provides improved prediction of retention factors, with the model standard error being reduced from 1.0 for the original HSM to 0.35 for the refined model (16 solutes, 551 columns). Furthermore, the number of retention factors with errors greater than 10% are reduced from 231 to 25. A revised metric for column similarity, F, is also proposed as a part of this work.