Study on detection of pesticide residues in tobacco based on hyperspectral imaging technology
The results show that combining spectral preprocessing and feature extraction algorithms with machine learning models can significantly enhance the performance of pesticide residue detection models and provide robust, efficient, and accurate solutions for food safety monitoring. This study provides a new technical means for the detection of pesticide residues in tobacco, which is of great significance for improving the efficiency and accuracy of food safety detection.
Authors
- Yaxi Liu (ORCID: https://orcid.org/0000-0001-6814-7218)
- Caixia Li (ORCID: https://orcid.org/0000-0003-2190-655X)
- Liang Zhang (ORCID: https://orcid.org/0000-0001-7082-8258)
- Min Liang (ORCID: https://orcid.org/0000-0002-6767-4232)
- Yu Lin (ORCID: https://orcid.org/0009-0001-8143-3689)
- Zhiqiang Wang
Institutions
- Sichuan Agricultural University (CN)
- Sichuan Academy of Agricultural Sciences (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2024-09-30
- DOI
- https://doi.org/10.3389/fpls.2024.1459886
- Citations
- 12
- Primary Topic
- Spectroscopy and Chemometric Analyses
- Type
- article
- Field-Weighted Citation Impact
- 2.83
Funders
- China National Tobacco Corporation
- Natural Science Foundation of Sichuan Province
- National Key Research and Development Program of China