Support vector regression based QSPR for the prediction of retention time of pesticide residues in gas chromatography–mass spectroscopy

Abstract

In this study, a quantitative structure–property relationship (QSPR) method was employed to predict the retention time (tR) of 368 pesticide residues in animal tissues separated by gas chromatography–mass spectroscopy (GC–MS). The variable selection method of genetic algorithm-partial least squares (GA-PLS) was employed to select most favorable subset of descriptors. The six descriptors selected using GA-PLS were used as inputs of PLS, ANN and SVM to predict the retention times. These descriptors are: number of nitrogen atoms, solvation connectivity index‐Chi 1, Balaban Y index, Moran autocorrelation‐lag 2/weighted by atomic Sanderson electronegativity, total absolute charge and radial distribution function-6.0/unweighted. The correlation coefficients, R, between experimental and predicted tR for the prediction set by PLS, ANN and SVM are 0.907, 0.963 and 0.985 respectively. Results obtained reveal the reliability and good predictability of nonlinear QSPR model to predict the retention time of pesticides. Comparison between the values of statistical parameters reveals the superiority of the SVM model over PLS and ANN ones.