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. © 2011.