Retention Indices for Programmed-Temperature Gas Chromatography of Polycyclic Aromatic Hydrocarbons: A QSRR Study

Abstract

The programmed-temperature gas chromatography retention index (I) of 60 compounds containing unsubstituted and substituted polycyclic aromatic hydrocarbons, is predicted by a statistically validated QSRR modeling approach. The applied multiple linear regression (ordinary least squares, OLS) is based on a variety of theoretical molecular descriptors selected by the genetic algorithms-variable subset selection procedure. The model was validated for predictivity by different internal and external validation approaches. For external validation the original data set was randomly split. The best two-dimensional model, developed on a reduced training set of 20 chemicals, has a predictivity of 99.67 % when applied on 40 validation chemicals (prediction set). Descriptors included in the QSRR model indicated the role of volume, dispersion and hydrophobic interactions in retention mechanism.