Prediction of Retention Behavior of Pesticides in Fruits and Vegetables in Low-Pressure Gas Chromatography–Time-of-Flight Mass Spectrometry

Abstract

Pesticides are substances or mixture of substances intended for preventing, destroying, repelling, or mitigating any pest. A pesticide may be a chemical substance, biological agent (such as a virus or bacterium), antimicrobial, disinfectant, or device used against any pest. Although the use of pesticides has many benefits, there are also drawbacks, such as potential toxicity to humans and other animals. A quantitative structure–retention relationship (QSRR) was developed using the partial least squares (PLS), kernel PLS (KPLS), and Levenberg–Marquardt artificial neural network (L-M ANN) approach for chemometrics study. The data which contained retention time (RT) of the 150 pesticides in tomato, strawberry, potato, orange, and lettuce samples were obtained by low-pressure gas chromatography–time-of-flight mass spectrometry. Genetic algorithm was employed as a factor selection procedure for PLS and KPLS modeling methods. By comparing the results, genetic algorithm–PLS descriptors are selected for L-M ANN. Finally, a model with a low prediction error and a good correlation coefficient was obtained by L-M ANN. The described model does not require experimental parameters and potentially provides useful prediction for the RT of new compounds. This is the first research on the QSRR of pesticides in fruits and vegetables using the chemometrics models.