Use of computer-assisted methods for the modeling of the retention time of a variety of volatile organic compounds: a PCA-MLR-ANN approach.

Abstract

A hybrid method consisting of principal component analysis (PCA), multiple linear regressions (MLR), and artificial neural network (ANN) was developed to predict the retention time of 149 C(3)-C(12) volatile organic compounds for a DB-1 stationary phase. PCA and MLR methods were used as feature-selection tools, and a neural network was employed for predicting the retention times. The regression method was also used as a calibration model for calculating the retention time of VOCs and investigating their linear characteristics. The descriptors of the total information index of atomic composition, IAC, Wiener number, W, solvation connectivity index, X1sol, and number of substituted aromatic C(sp(2)), nCaR, appeared in the MLR model and were used as inputs for the ANN generation. Appearance of these parameters shows the importance of the dispersion interactions in the mechanism of retention. Comparison of the MLR and 5-2-1 ANN models indicates the superiority of the ANN over that of the MLR model. The values of 0.913 and 0.738 were obtained for the standard error of prediction set of MLR and ANN models, respectively.