Chemometric modeling to predict retention times for a large set of pesticides or toxicants using hybrid genetic algorithm/multiple linear regression approach

Abstract

Purpose – The purpose of this paper is to predict the retention times of 84 pesticides or toxicants. Design/methodology/approach – Quantitative structure – retention relationship analysis was performed on a set of 84 pesticides or toxicants using a hybrid approach genetic algorithm/multiple linear regression (GA/MLR). Findings – A model with six descriptors was developed using as independent variables. Theoretical descriptors derived from Spartan and Dragon softwares when applying GA/MLR approach. Originality/value – A six parameter linear model developed by GA/MLR, with R² of 90.54, Q² of 88.15 and S of 0.0381 in Log value. Several validation techniques, including leave-many-out cross-validation, randomization test, and validation through the test set, illustrated the reliability of the proposed model. All of the descriptors involved can be directly calculated from the molecular structure of the compounds, thus the proposed model is predictive and could be used to estimate the retention times of pesticides or toxicants.

Prediction of Analyte Retention Time in Liquid Chromatography

“…The purpose of these predictions is to eliminate or at least reduce multiple false positives, especially of isobaric compounds. These applications include the identification of veterinary drugs in food products, metabolite identification in nontargeted metabolomics, ,,,, nontargeted identification of novel environmental contaminants, , identification of erectile dysfunction drugs and their analogues, nontargeted screening of steroids, nontargeted lipidomics, profiling of acylcarnitines and their derivatives, pesticides, elution order, and dissociating compounds …”