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Rapid and accurate determination of prohibited components in pesticides based on near infrared spectroscopy

文献类型： 外文期刊

作者： Xie, Leiying 1 ; Zhu, Jianguo 1 ; Wang, Yajing 1 ; Wang, Na 1 ; Liu, Feng 1 ; Chen, Zilei 5 ; Wang, Ping 8 ; Wang, Shaowei 1 ; Shen, Xuechu 1 ;

作者机构： 1.Chinese Acad Sci, Shanghai Inst Tech Phys, State Key Lab Infrared Phys, Shanghai 200083, Peoples R China

2.Univ Shanghai Sci & Technol, Sch Mat Sci & Engn, Shanghai 200093, Peoples R China

3.Shanghai Normal Univ, Dept Phys, Shanghai 200234, Peoples R China

4.ShanghaiTech Univ, Sch Phys Sci & Technol, Shanghai 201210, Peoples R China

5.Shandong Acad Agr Sci, Inst Agr Qual Standard & Test Technol Agroprod, Jinan 250100, Peoples R China

6.Shanghai Engn Res Ctr Energy Saving Coatings, Shanghai 200083, Peoples R China

7.Univ Chinese Acad Sci, Beijing 100049, Peoples R China

8.Chinese Acad Sci, Shanghai Inst Ceram, Shanghai 201899, Peoples R China

关键词： Doped pesticide determination; Quantitative analysis; Back propagation neural network

期刊名称：INFRARED PHYSICS & TECHNOLOGY （ 影响因子：2.638； 五年影响因子：2.581 ）

ISSN： 1350-4495

年卷期： 2022 年 121 卷

页码：

收录情况： SCI

摘要： Highly toxic pesticides are forbidden because of threatening to human lives, property, and the environment. However, some of them are still used illegally by added into normal pesticides to enhance the effectiveness and lower the cost. Therefore, it is very important to detect the illegally added ingredients in pesticides, such as Isofenphos-methyl in Tolfenpyrad. In this research, a rapid and accurate method for determining the concentration of Isofenphos-methyl in Tolfenpyrad is studied by near infrared spectroscopy with only nine characteristic wavelengths. Different pre-treating methods of direct orthogonal signal correction and first-order derivative on raw spectra and analysis methods of back propagation neural network and partial least squares are investigated to establish quantitative analysis models for comparison. Among them, the back propagation neural network model pre-treated with the direct orthogonal signal correction method has good prediction accuracy and prevents the data from being over-corrected. The correlation coefficients of the model calibration set and prediction set are as high as 0.999 and 0.989, respectively. Root Mean Square Error of Prediction and Ratio of Prediction to Deviation are 0.27% and 9.17, respectively. The prediction concentration limit can be as low as 0.5%. The results show that the direct orthogonal signal correction pre-treating method combined with back propagation neural network can be used for precisely quantitative determination of pesticide doping concentration. It provides an effective approach for rapid and accurate determination of prohibited components in pesticides.

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