QSPR analysis for the retention index of flavors and fragrances on a OV-101 column

Abstract

A predictive quantitative structure`property relationships (QSPR) is developed for modeling the retention index measured on the OV-101 glass capillary gas chromatography column, in a set of 1208 flavor and fragrance compounds. The 4885 molecular descriptors are calculated using the Dragon software and then are simultaneously analyzed through multivariable linear regression analysis using the replacement method (RM) variable subset selection technique. We proceed in three steps, the first one by considering all descriptor blocks, the second one by excluding conformational descriptors blocks, and the last one by analyzing only 3D-descriptors families. The models are properly validated through an external test set of compounds. Cross-validation methods such as leave-one-out and leave-more-out are applied, togetherwith Y-randomization and applicability domain analysis. The results clearly suggest that 3D-descriptors do not offer relevant information for modeling the retention index, while a topological index such as the solvation connectivity index of first order has a high relevance for this purpose.