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当前位置: 首页 > 文章 > Detection and classification of pesticide residues in dandelion (Taraxacum officinale L.) by electronic nose combined with chemometric approaches 国际农业与生物工程学报 2023,16 (5)
Position: Home > Articles > Detection and classification of pesticide residues in dandelion (Taraxacum officinale L.) by electronic nose combined with chemometric approaches International Journal of Agricultural and Biological Engineering 2023,16 (5)

Detection and classification of pesticide residues in dandelion (Taraxacum officinale L.) by electronic nose combined with chemometric approaches

作  者：
Qiao Jianlei;Xinhong Jiang;Weng Xiaohui;Cui Hongbo;Chang Liu;Zou Yuanjun;Hailing Yu;Yaokai Feng;Zhiyong Chan
单  位：
4. College of Resource and Environmental Sciences/Key Laboratory of Sustainable Utilization of Soil Resources in The Commodity Grain Bases of Jilin Province, Jilin Agricultural University, Changchun, 130018, China;2. College of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China;5. Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China;1. College of Horticulture, Jilin Agricultural University, Changchun 130118, China; 6. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, Chin;3. College of Medical Information, Changchun University of Chinese Medicine, Changchun 130117, China
关键词：
classification;dandelion;electronic nose;pesticide residue;Taraxacum officinale L
摘  要：
In this study, for the first time establish a suitable pesticide residue detection system for dandelion (Taraxacum officinale L.) based on electronic nose to determine and study the concentration of pesticide residue in dandelion. Dandelions were sprayed with different concentrations of pesticides (avermectin, trichlorfon, deltamethrin, and acetamiprid), respectively. Data collection was performed by application of an electronic nose equipped with 12 metal oxide semiconductor (MOS) sensors. Data analysis was conducted using different methods including BP neural network and random forest (RF) as well as the support vector machine (SVM). The results showed the superior effectiveness of SVM in discrimination and classification of non-exceeding MRLs and exceeding MRLs standards. Moreover, the model trained by SVM has the best performance for the classification of pesticide categories in dandelion, and the total classification precision was 91.7%. Classification of trichlorfon was better in all the methods when compared with avermectin, deltamethrin, and acetamiprid. Keywords: electronic nose, dandelion, Taraxacum officinale L., pesticide residue, classification DOI: 10.25165/j.ijabe.20231605.7886 Citation: Qiao J L, Jiang X M, Weng X H, Cui H B, Liu C, Zou Y J, et al. Detection and classification of pesticide residues in dandelion (Taraxacum officinale L.) by electronic nose combined with chemometric approaches. Int J Agric & Biol Eng, 2023; 16(5): 181–188.

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