A simple approach to the prediction of soil sorption of organophosphorus pesticides.

Organophosphorus pesticides (OP) affect the crops and environments, and the reliable approach to the prediction of soil sorption of pesticides is required. In this respect, we proposed a simple Chemometrics approach, in which the Tchebichef image moment (TM) method was used to extract useful information from the greyscale images of molecular structures and the quantitative model was established by stepwise regression to predict the soil sorption of OPs. Different squared correlation coefficients including the leave-one-out cross-validation (LOO-CV) (Q2) that concerns the training set and the (R2test) which concerns the external independent test set are more than 0.96. This reflects that the established model has considerably high accuracy and reliability. Compared with the literature on the strategies of quantitative structure-property relationship (QSPR), the proposed method is more suitable, in which the established model shows a high predictive ability. Our study provides another effective approach to predict the soil sorption of OPs and also extends the innovative pathway of QSPR modelling.