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Genetic algorithm and partial least square(GA-PLS),kernel PLS(KPLS) and Levenberg-Marquardt artificial neural network(L-M ANN) techniques were used to investigate the correlation between retention time(RT) and descriptors for 15 nanoparticle compounds obtained by the comprehensive two-dimensional gas chromatography system(GC × GC).Application of the dodecanethiol monolayer-protected gold nanoparticle(MPN) column was for a high-speed separation as the second column of GC × GC.The L-M ANN model with the final optimum network architecture of [13-5-1] gave a significantly better performance than the other models.This is the first research on the quantitative structure-retention relationship(QSRR) of nanoparticle compounds using the GA-PLS,GA-KPLS and L-M ANN.
Genetic algorithm and partial least square (GA-PLS), kernel PLS (KPLS) and Levenberg-Marquardt artificial neural network (LM ANN) techniques were used to investigate the correlation between retention time (RT) and descriptors for 15 nanoparticle compounds obtained by the comprehensive two-dimensional gas chromatography system (GC × GC). Application of the dodecanethiol monolayer-protected gold nanoparticle (MPN) column was for a high-speed separation as the second column of GC × GC. LM ANN model with the final optimum network architecture of [13-5-1] gave a significantly better performance than the other models. This is the first research on the quantitative structure-retention relationship (QSRR) of nanoparticle compounds using the GA-PLS, GA-KPLS and LM ANN .