Pesticides are considered a vital component of modern farming, playing major roles in maintaining high agricultural productivity. Pesticide recovery rates in vegetables and fruits determined using GC/MS depends on various factors including the matrix effect and chemical interactions between pesticides and mixing compounds in crops. In this study, the recovery rate of a pesticide is defined by a ratio of peak area of 50 ppb spiked in a crop sample to that in the solvent standard calibration curve. The estimation of recovery rates of pesticides in crops leads to evaluation of precise contents of them in the crops. In the present study, we performed regression models of the recovery rates based on molecular descriptors using R-packages rcdk and caret. Each of the chemical structures of 248 pesticides was converted to 174 molecular descriptors, then, for 7 crops, we created 69 ordinary and 20 ensemble learning regression models for estimating the recovery rates from the molecular descriptors using R-package caret. In the present study, two machine learning regression methods called mSBC and xgbLinear performed the best in view of prediction rates and execution times. In those two regression models predictions of recovery rates of pesticides are carried out in local distribution of chemical properties out of the 174 molecular descriptors. This concludes that closely related pesticides in the chemical space have also very similar recovery rates.
