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