PREDICTION OF RETENTION OF PESTICIDES IN REVERSED-PHASE HIGH-PERFORMANCE LIQUID CHROMATOGRAPHY USING CLASSIFICATION AND REGRESSION TREE ANALYSIS AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS

Abstract

A quantitative structure-retention relationship (QSRR) was developed to predict the retention of some pesticides in reversed-phase high-performance liquid chromatography with different mobile phase composition. A set of 1497 zero-to three-dimensional descriptors were used for each molecule in the data set. Classification and regression tree (CART) was successfully used as a descriptor selection method. In addition to mobile phase composition, two very simple descriptive descriptors (octanol-water partition coefficient and average molecular weight) were also selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). The root mean square errors for the training, validation, and test sets were 0.027, 0.034, and 0.036, respectively. In comparison with artificial neural network method, the results showed CART-ANFIS is a powerful model for prediction of retention of pesticides.

Keywords:

• adaptive neuro-fuzzy inference system
• classification and regression tree
• pesticides
• quantitative structure-retention relationship
• retention model
• reversed-phase liquid chromatography

ACKNOWLEDGMENT

The authors acknowledge to the Research Council of Damghan University for the partial support of this work.

Notes

Data samples in the training, validation, and test set are given in plain, underlined, and bold characters, respectively.