Model prediction of herbicide residues in soybean oil: Relationship between physicochemical properties and processing factors

Food Chem. 2022 Feb 15:370:131363. doi: 10.1016/j.foodchem.2021.131363. Epub 2021 Oct 7.

Abstract

The distribution and processing factors (PFs) of herbicides in cold-/hot-pressed soybean samples (n = 3) were studied on the laboratory scale. The hot-pressing process was found to have a significant effect on herbicide degradation in soybean samples. Specifically, for highly water-soluble pesticides with pKow > 2 in soybean oil, the PF values were generally > 1. Nonlinear curve fitting revealed that the PFs of herbicides in soybean oil were positively correlated with their octanol-water partition coefficients, but negatively correlated with their water solubility and melting points. A principal component analysis confirmed the dominant parameters among the herbicide PFs during soybean oil production. Using the physicochemical parameters of pesticides, the developed multiple linear regression model gave a fitting accuracy of ≥0.80 for predicting the theoretical PF values of pesticides in soybean oil products (0.39 < RMSE < 0.58). Thus, this model may be applicable for safety risk assessments and establishing maximum residue limits for pesticides in processed products.

Keywords: Cold-/hot-pressing; Melting point; Multiple linear regression; Octanol–water partition coefficient; Water solubility.

MeSH terms

  • Herbicides*
  • Octanols
  • Pesticides* / analysis
  • Solubility
  • Soybean Oil

Substances

  • Herbicides
  • Octanols
  • Pesticides
  • Soybean Oil