We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Rapid detection of carbendazim residue in tea by machine learning assisted electrochemical sensor.
- Authors
Tang, Man; Guo, Junying; Shen, Zhen
- Abstract
The presence of pesticide residues in agricultural products, such as tea, poses significant health risks to consumers and has become a major concern for the food industry. Carbendazim, a widely used fungicide, has been identified as one of the harmful contaminants in tea. Rapid and accurate detection of carbendazim residues is therefore essential for ensuring food safety and quality. In this study, we propose a novel machine learning-assisted electrochemical sensor based on a CuO/reduced graphene oxide (rGO)/poly N-phenylglycine (NPAN) composite electrode for the rapid detection of carbendazim residues in tea. The CuO/rGO/NPAN electrode offers enhanced selectivity and sensitivity, enabling reliable detection of carbendazim even in the presence of other pesticides. By systematically evaluating various machine learning models and preprocessing techniques, we optimize the sensor's performance and achieve accurate classification and identification of carbendazim residues. Our research contributes to the field of food safety assessment by introducing a novel electrode design and demonstrating the effectiveness of machine learning-assisted electrochemical sensors for the detection of carbendazim residues in tea.
- Subjects
ELECTROCHEMICAL sensors; CARBENDAZIM; PESTICIDE residues in food; MACHINE learning; AGRICULTURAL wastes; INTRUSION detection systems (Computer security); FARM produce
- Publication
Journal of Food Measurement & Characterization, 2023, Vol 17, Issue 6, p6363
- ISSN
2193-4126
- Publication type
Article
- DOI
10.1007/s11694-023-02112-2