QSRR analysis of capacity factor of nanoparticle compounds

Genetic algorithm and partial least square (GA–PLS) and Levenberg–Marquardt artificial neural network (L–M ANN) techniques were used to investigate the correlation between capacity factor (k′) and descriptors for 40 nanoparticle compounds which obtained by comprehensive two-dimensional gas chromatography (GC × GC) stationary phases consisting of thin films of gold-centered monolayer protected nanoparticles (MPNs) system. The applied internal (leave-group-out cross-validation (LGO-CV)) and external (test set) validation methods were used for the predictive power of models. The results indicate that L–M ANN can be used as an alternative modeling tool for quantitative structure–retention relationship (QSRR) studies. This is the first research on the QSRR of the nanoparticle compounds using the L–M ANN.