Prediction of two-dimensional gas chromatography time-of-flight mass spectrometry retention times of 160 pesticides and 25 environmental organic pollutants in grape by multivariate chemometrics methods

Abstract

A B S T R A C TA quantitative structure-retention relation (QSRR) study was conducted on the retention times of 160 pesticides and 25 environmental organic pollutants in wine and grape. The genetic algorithm was used as descriptor selection and model development method. Modeling of the relationship between the selected molecular descriptors and retention time was achieved by linear (partial least square; PLS) and nonlinear (kernel PLS: KPLS and Levenberg-Marquardt artificial neural network; L-M ANN) methods. The QSRR models were validated by cross-validation as well as application of the models to predict the retention of external set compounds, which did not have contribution in model development steps. Linear and nonlinear methods resulted in accurate prediction whereas more accurate results were obtained by L-M ANN model. The best model obtained from L-M ANN showed a good R 2 value (determination coefficient between observed and predicted values) for all compounds, which was superior to those of other statistical models. This is the first research on the QSRR of the compounds in wine and grape against the retention time using the GA-KPLS and L-M ANN.

Artificial intelligence–based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method

“…The application of this model for two NIR datasets showed that the technique provides good prediction performance, and it outperformed the classical models. Amini et al [16] reported that QSRR was used to determine and predict the retention factor of 160 different pesticides and 25 environmental organic pollutants in grape and wine. Modeling of the relationship between the selected molecular descriptors and retention time was achieved by linear [partial least square(PLS)] and nonlinear [kernel PLS(KPLS) and Levenberg-Marquardt ANN(L-M ANN)] methods.…”