Model for prediction of pesticide residues in soybean oil using partial least squares regression with molecular descriptors selected by a competitive adaptive reweighted sampling algorithm-Reference-Cited by-同舟云学术

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Model for prediction of pesticide residues in soybean oil using partial least squares regression with molecular descriptors selected by a competitive adaptive reweighted sampling algorithm

Published:2024-09 Issue:3 Volume:2 Page:100053
ISSN:2949-7981
Container-title:Agriculture Communications
language:en
Short-container-title:Agriculture Communications

Author:

Shi Yonghong,Wang Fengzhong,Xie Hong,Fan Bei,li Long,Kong Zhiqiang,Huang Yatao,Wang Zhipeng,Lei Daoyong,Li Minmin

Funder

Liaoning Academy of Agricultural Sciences

Beijing Nova Program

National Aerospace Science Foundation of China

Taishan Scholar Project of Shandong Province

Publisher

Elsevier BV

Reference44 articles.

1. Multiclass pesticide determination in olives and their processing factors in olive oil: comparison of different olive oil extraction systems;Amvrazi;J Agric Food Chem,2008

2. Residual levels of five pesticides in peanut oil processing and chips frying;Duan;J Sci Food Agric,2022

3. Residue behaviors, degradation, processing factors, and risk assessment of pesticides in citrus from field to product processing;Tang;Sci Total Environ,2023

4. Effects of storage and processing on residue levels of chlorpyrifos in soybeans;Zhao;Food Chem,2014

5. Food processing a tool to pesticide residue dissipation – a review;Kaushik;Food Res Int,2009

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