Rapid Detection of Pesticide Residues on Navel Oranges by Fluorescence Hyperspectral Imaging Technology Combined With Characteristic Wavelength Selection
HAO Jie, DONG Fu-jia, WANG Song-lei*, LI Ya-lei, CUI Jia-rui, LIU Si-jia, LÜ Yu
School of Food & Wine, Ningxia University, Yinchuan 750021, China
Abstract：In this study, fluorescence hyperspectral imaging technology identified different concentrations of chlorpyrifos and carbendazim on the surface of navel oranges. Hyperspectral images of the concentrations of chlorpyrifos at 0, 0.5, 1 and 2 mg·kg-1 and carbendazim at 0, 1, 3 and 5 mg·kg-1 were acquired by a hyperspectral imaging system (392~998.2 nm) excited by a xenon light source. The sample’s region of interest (ROI) was captured by ENVI software. Raw spectral data were pre-processed by a spectral pre-processing methods, including SG, SNV and FD. The interval variable iterative spatial shrinkage (iVISSA), uninformative variable elimination algorithm (UVE) and competitive adaptive reweighted sampling (CARS) were used for the primary extraction of feature wavelengths and the two-dimensional correlation spectroscopy (2D-COS) method for secondary extraction of feature wavelengths. PLS-DA and PCA-LDA model developed primaryand secondary feature wavelength extraction at different concentrations of chlorpyrifos and carbendazim residues on the surface of navel oranges. 3 methods studied the spectral pretreatment. The results showed that the model effect of SG methods was best. A total of 26 feature wavelengths were extracted by the iVISSA method for the spectral data using the SG chlorpyrifos; A total of 30 feature wavelengths were extracted by the CARS method for the spectral data using the SG method of carbendazim. The 2D-COS algorithm was used for the secondary extraction of 26 and 30 feature wavelengths, resulting in 10 and 12 feature wavelengths, respectively. Discriminant models based on spectral data of primary and secondary extraction of feature wavelengths were established to identify the samples. The results showed that the PCA-LDA model based on iVISSA-2D-COS was the best with the calibration set and prediction set discrimination rates of 98.61% and 95.83% for different concentrations of chlorpyrifos. The PCA-LDA model based on CARS-2D-COS was the best with the calibration set and prediction set discrimination rates of 97.22% and 95.83% for different concentrations of carbendazim, respectively, which were higher than the discrimination rates of full-band spectral data and once-extraction feature spectral data. In this study, secondary extraction of the optimal feature wavelengths by 2D-COS has developed discrimination models, and the results can provide some reference for rapid and non-destructive discrimination for different concentrations of pesticide residues on the surface of navel oranges.
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