Detection of Pesticide Residues in Mulberey Leaves Using Vis-Nir Hyperspectral Imaging Technology

作者：Sun Jun^*; Jiang Shuying; Zhang Meixia; Mao Hanping; Wu Xiaohong; Li Qinglin

来源：Journal of Residuals Science & Technology, 2016, 13(2): S125-S131.

DOI：10.12783/issn.1544-8053/13/2/s18

Nonstandard use of pesticides often causes poisoning incidents of silkworm, which is a serious threat to the development of sericulture industry. In view of this, it is very urgent to study new non-destructive testing methods that can detect pesticide residues in mulberry leaves rapidly and accurately. In this paper, six groups of mulberry leaves (144 mulberry leaves in total), on which chlorpyrifos pesticide of six different concentrations had been sprayed respectively, were chosen as experimental samples, and their hyperspectral images in 390-1050 nm were acquired by hyperspectral imaging devices. The region of interest from hyperspectral image was selected, and five sensitive wavelengths including 561.25, 680.86, 706.58, 714.32, and 724.66 nm were determined by correlation coefficients between pesticides residues and spectral reflectances. Further, based on multiple linear regression (MLR) and support vector regression (SVR), the prediction models of pesticide residues in mulberry leaves were established respectively to fit the experimental data. The results showed that the root mean square error (RMSE) and coefficient of determination (R2) of prediction set of MLR model were 47.165 and 0.637 respectively, and those of prediction set of SVR model were 27.719 and 0.874 respectively. Therefore, hyperspectral imaging technology together with SVR prediction model could accurately detect the pesticide residues in mulberry leaves.

• 出版日期2016
• 单位江苏大学