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

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.