Study of the Retention Time of Nanoparticle Compounds by Quantitative Structure Retention Relationship

Abstract

The quantitative structure-retention relationship (QSRR) of nanoparticle compounds against the comprehensive 2-D gas chromatography system (GC × GC) retention time (RT) was studied. Application of the dodecanethiol monolayer-protected gold nanoparticle (MPN) column was for a high-speed separation as the second column of GC × GC. A suitable set of molecular descriptors was calculated and the genetic algorithm (GA) was employed to select those descriptors that resulted in the best-fit models. The partial least squares (PLS), the kernel PLS (KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) were utilized to construct the linear and nonlinear QSRR models. The models were validated using leave-Groupout cross validation LGO-CV. The results indicate that L-M ANN can be used as an alternative modeling tool.