Nanozyme-Powered Multimodal Sensing for Pesticide Detection
The detection of pesticide residues in food is crucial for ensuring food safety, safeguarding public health, and promoting sustainable development. Overusing pesticides on agricultural crops can lead to the emergence of various diseases. Traditional methods for detecting pesticides offer high precis...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Foods |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-8158/14/11/1957 |
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| Summary: | The detection of pesticide residues in food is crucial for ensuring food safety, safeguarding public health, and promoting sustainable development. Overusing pesticides on agricultural crops can lead to the emergence of various diseases. Traditional methods for detecting pesticides offer high precision with limitations like high cost, the requirement of expert technicians, and tedious analytical procedures. To address these issues, nanozymes have been widely applied due to their advantages such as low cost, high stability, and high sensitivity. This review summarizes the research progress of nanozymes in the detection of pesticide residues in food over the last decade, focusing on the synthesis strategies and catalytic mechanisms of carbon-based, metal-based, metal-oxide-based, metal–organic framework (MOF)-based, fluorescence-based, and other X-based nanozymes. This review covers the application of multimodal sensing based on nanozymes in the detection of pesticides, including colorimetric/fluorescence, fluorescence/photothermal, photothermal/colorimetric, and other multimodal sensing techniques. Finally, this review discusses the main challenges currently faced by nanozymes in the detection of pesticides and the current applications of using AI with nanozymes. It also presents future development prospects, with the aim of providing references for the selection of X-based nanozymes and the choice of appropriate detection methods when dealing with traditional and new pesticides in combination with AI. |
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| ISSN: | 2304-8158 |