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

文献类型： 外文期刊

第一作者： Zhang, Jia

作者： Zhang, Jia;Li, Minmin;Quan, Rui;Gao, Tengfei;Fan, Bei;Wang, Fengzhong;Kong, Zhiqiang;Bai, Tiecheng;Duan, Lifang;Liu, Yongguo

作者机构：

关键词： Cold-/hot-pressing; Multiple linear regression; Octanol-water partition coefficient; Water solubility; Melting point

期刊名称：FOOD CHEMISTRY （ 影响因子：9.231； 五年影响因子：8.795 ）

ISSN： 0308-8146

年卷期： 2022 年 370 卷

页码：

收录情况： SCI

摘要： 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 pK(ow) > 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.

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