Untitled

Abstract

A quantitative structure-retention relation (QSRR) study was conducted on the range-scaling transformation (Xa) of the nanoparticle compounds which obtained by comprehensive two-dimensional gas chromatography (GC×GC) stationary phases consisting of thin films of the gold-centered monolayer protected nanoparticles (MPNs) system. The genetic algorithm was used as descriptor selection and model development method. Modeling of the relationship between the selected molecular descriptors and the retention time was achieved by linear (partial least square; PLS) and nonlinear (Levenberg-Marquardt artificial neural network; L-M ANN) methods. Linear and nonlinear methods resulted in an accurate prediction whereas more accurate results were obtained by L-M ANN model.