Nanoscale substance–integrated optical sensing platform for pesticide detection in perishable foods
Engineered optical sensing platforms are emerging as promising technologies for on-demand detection and quantification of target molecules. In the past, nanoscale substance–integrated optical sensing platforms were closely connected with biomedical applications. However, their use has recently expanded into food and agriculture to overcome the drawbacks of conventional analytic tools while achieving the detection of toxic chemical residues. This review discusses current examples and perspectives on the detection of pesticides/herbicides residue in perishable food using a nanoscale substance–supported novel optical sensing platform. Additionally, representative types of nanoscale substance–supported optical sensing platforms applied to food and agriculture with their corresponding examples are also briefly illustrated. This review provides an in-depth analysis of the prospect and challenge, confirming their promising advance and potential for pesticides/herbicides detection in food and agriculture. The authors envision that nanoscale substances–integrated optical sensing platforms will play a significant role in enhancing food safety in the future. • Pesticide/herbicide residues in perishable foods pose risks to human health. • Engineered optical systems overcome the drawbacks of conventional analytic tools. • Nanoscale substances foster enhanced analytic sensitivity with less complexity. • Nanoscale substance-integrated optical sensing platforms contribute to food safety.
Authors
- Md Wadud Ahmed (ORCID: https://orcid.org/0000-0003-4888-7002)
- Mohammed Kamruzzaman (ORCID: https://orcid.org/0000-0002-3525-3521)
- Dong Hoon Lee (ORCID: https://orcid.org/0000-0002-5769-6585)
Institutions
- University of Illinois Urbana-Champaign (US)
Publication Details
- Journal
- Current Opinion in Food Science
- Published
- 2024-10-02
- DOI
- https://doi.org/10.1016/j.cofs.2024.101227
- Citations
- 5
- Primary Topic
- Biosensors and Analytical Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.92