Discrimination of pesticide residues in lettuce based on chemical molecular structure coupled with wavelet transform and near infrared hyperspectra

作者：Sun, Jun; Zhou, Xin^*; Mao, Hanping; Wu, Xiaohong; Zhang, Xiaodong; Li, Qinglin

来源：Journal of Food Process Engineering, 2017, 40(4): e12509.

DOI：10.1111/jfpe.12509

• feature extraction
• food safety
• lettuce
• pesticide detection
• wavelet transform

To facilitate more quickly and effectively detect the types of pesticide residues on the surface of lettuce, a method involving the chemical molecular structure coupled with wavelet transform (CMS-WT) was proposed to extract the characteristic wavelength. Five different kinds of pesticide residues were sprayed on the surface of lettuce, respectively, dimethoate, acephate, phoxim, dichlorvos, avermectin (the ratio of pesticides and water were 1:1000). In addition, the near infrared hyperspectral image information of 200 samples in five different kinds of pesticides residue in lettuce were achieved by the NIR hyperspectral imaging system (870-1780 nm). The region of interest (ROI) in hyperspectral image of samples was selected to get the near infrared spectral data by the software of ENVI. Furthermore, CMS-WT was used to extract the most influential wavelengths. Four characteristic intervals were extracted by comparing the different of pesticides in chemical molecular structures, respectively, 8751050 nm, 10501250 nm, 13501550 nm, 16501780 nm. Further, the best combination of eight features were selected according to the reorder of the size of the singular value by wavelet transform algorithm using db6 as wavelet basis function, respectively, 919.18, 944.25, 972.25, 1194.20, 1363.81, 1426.69, 1673.29, 1680.74 nm. Finally, SVM model was established according to the extracted characteristic spectral data. The results showed that the calibration and prediction accuracy of SVM model established by the best combination of eight features were all achieved 100%. It confirms that the CMS-WT feature extraction algorithm is feasible and effective for building models of different pesticide residues in lettuce. @@@ Practical applicationsWell understanding the effect of pesticide residues to biological structure is very important for revelation of novel biological function and mechanism of action of the protein. To facilitate more quickly and effectively detect the types of pesticide residues on the surface of lettuce, a method involving the chemical molecular structure coupled with wavelet transform (CMS-WT) was proposed to extract the characteristic wavelength in this article. It confirms that the CMS-WT feature extraction algorithm is feasible and effective for building models of different pesticide residues in lettuce.

• 出版日期2017-8
• 单位江苏大学