Internet Archive Scholar

Identification of lambda-cyhalothrin residues on Chinese cabbage using fuzzy uncorrelated discriminant vector analysis and MIR spectroscopy

Xiaohong Wu, 1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China, Tingfei Zhang, Bin Wu, Haoxiang Zhou, 2. High-tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, Jiangsu, China, 3. Department of Information Engineering, Chuzhou Polytechnic, Chuzhou, 239000, Anhui, China, 4. Research Institute of Zhejiang University-Taizhou, Taizhou 317700, Zhejiang, China
2022 International Journal of Agricultural and Biological Engineering  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (367.8 kB)
https://web.archive.org/web/20220716135122/https://www.ijabe.org/index.php/ijabe/article/download/6486/pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL. The file type is application/pdf.

Excessive pesticide residues on Chinese cabbage will be harmful to people's health. Therefore, an identification system was designed for qualitative analysis of lambda-cyhalothrin residues on Chinese cabbage leaves. In order to extract discriminant information from mid-infrared (MIR) spectra of Chinese cabbage effectively, fuzzy uncorrelated discriminant vector (FUDV) analysis was proposed by introducing the fuzzy set theory into uncorrelated discriminant vector (UDV) analysis. In this system,
more » ... he Cary 630 FTIR spectrometer was used to scan four samples of Chinese cabbage with different concentrations of lambda-cyhalothrin. The MIR spectra were preprocessed by standard normal variable (SNV) and Savitzky-Golay smoothing (SG). Next, the high-dimensional MIR spectra were processed for dimension reduction by principal component analysis (PCA). Furthermore, UDV, FUDV, and some other discriminant analysis algorithms were used for feature extraction, respectively. Finally, the K-nearest neighbor (KNN) classifier was employed to classify the data. The experimental results showed that when FUDV was used as the feature extraction algorithm, the identification system reached the maximum classification accuracy of 100%. The results indicated that FUDV combined with MIR spectroscopy was an effective method to identify lambda-cyhalothrin residues on Chinese cabbage.
doi:10.25165/j.ijabe.20221503.6486 fatcat:zloff4sdxnejnn5gyszhm6iscy
DOAJ
Citation

Xiaohong Wu, 1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China, Tingfei Zhang, Bin Wu, Haoxiang Zhou, 2. High-tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, Jiangsu, China, 3. Department of Information Engineering, Chuzhou Polytechnic, Chuzhou, 239000, Anhui, China, 4. Research Institute of Zhejiang University-Taizhou, Taizhou 317700, Zhejiang, China. "Identification of lambda-cyhalothrin residues on Chinese cabbage using fuzzy uncorrelated discriminant vector analysis and MIR spectroscopy." International Journal of Agricultural and Biological Engineering 15.3 (2022) 217-224