Application of MLR, PLS and Artificial Neural Networks for Prediction of GC/ECD Retention Times of Chlorinated Pesticides, Herbicides, and Organohalides

Quantitative structure–retention relationship (QSRR) models correlating the retention times of diverse chlorinated pesticides, herbicides, and organohalides in gas chromatography/electron capture detector (GC/ECD) system and their structures were developed based on different multivariate regression techniques by using molecular structural descriptors.Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS) regression and Artificial Neural Networkwith batch back propagation algorithm (BBP-ANN).The stepwise regression using SPSS was used for the selection of the variables that resulted in the best-fitted models. The aim of this paper was to compare the performances of different linear and nonlinear multivariate calibration techniques. The predictive quality of the QSRR models were tested for an external prediction set of 12 compounds randomly chosen from 38 compounds. The best model obtained from the training set based onhighest external predictive R 2 value and lowest RMSEP value also showed good internal predictive power. The ANN method with Batch Back Propagation (BBP) algorithm was used to model the structure-retention relationships, more accurately. The squared regression coefficients of prediction for the MLR, PLS and ANN models were 0.951, 0.948 and 0.968, respectively