Structural characterization and prediction of Kovats retention indices (RI) for alkylbenzene compounds

Abstract

A new molecular structural characterization (MSC) method called the molecular vertex eigenvalue correlative index (MVECI) is constructed and used to describe the structures of 122 alkylbenzene compounds. Through multiple linear regression (MLR) and stepwise multiple regression (SMR), a quantitative structure-retention relationship (QSRR) model with correlation coefficient (R) of 0.995 is obtained. Through partial least-square regression (PLS), another QSRR model with correlation coefficient (R) of 0.991 is obtained. The estimation stability and prediction ability of the two models are strictly analyzed by both internal and external validations. For the internal validation, the cross-validation (CV) correlation coefficients (R_CV) of the two models are 0.993 and 0.988. For the external validation, the correlation coefficients (R_test) of the two models are 0.996 and 0.995, respectively. The results show that the stability and predictability of the models are good, and the molecular vertex eigenvalue correlative index can successfully describe the structures of alkylbenzene compounds.