AN IMPROVED QSPR STUDY OF REVERSE FACTOR OF NANOPARTICLES IN ROADSIDE ATMOSPHERE ON KERNEL PARTIAL LEAST SQUARES AND GENETIC ALGORITHM

Abstract

Thermal desorption-comprehensive two-dimensional gas chromatography high-resolution time-of-flight mass spectrometry (TD–GC × GC–HRTOF-MS) is one of the most powerful tools in analytical nanoparticle compounds. Genetic algorithm and partial least square (GA-PLS) and kernel PLS (GA-KPLS) models were used to investigate the correlation between reverse factor (RF) and descriptors for 50 nanoparticles fraction with a diameter of 29–58 nm in roadside atmosphere which obtained by TD–GC×GC–HRTOF-MS. The correlation coefficient leave-group-out cross validation (LGO-CV (Q2)) of prediction for the GA-PLS and GA-KPLS models for training and test sets were (0.761 and 0.718) and (0.825 and 0.814), respectively, revealing the reliability of these models. This is the first research on the quantitative structure-property relationship (QSPR) of the nanoparticles in roadside atmosphere using the GA-PLS and GA-KPLS.