Genetic algorithm and partial least square (GA-PLS), kernel PLS (GA-KPLS) 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 correlation coefficient LGO-CV (Q2) between experimental and predicted k\' for training set by GA-PLS, GA-KPLS and L-M ANN was 0.872, 0.931 and 0.981, respectively. This indicates that L-M ANN can be used as an alternative modeling tool for quantitative structure–retention relationship (QSRR) studies.

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