Treffer: Emerging applications of biorecognition elements-based optical biosensors for food safety monitoring.

Title:
Emerging applications of biorecognition elements-based optical biosensors for food safety monitoring.
Source:
Discover Sensors; Dec2025, Vol. 1 Issue 1, p1-26, 26p
Database:
Complementary Index

Weitere Informationen

Several concerns have emerged regarding food safety due to the widely used conventional detection technologies. However, in recent times continuous research efforts have devised alternative and viable technologies for rapid identification and detection of food contaminants. Optical-based biosensors which are largely dependent on biorecognition elements (BREs or bioreceptors) have thus attracted considerable attention among alternative technologies. This review summarizes the properties and suitability of three bioreceptors (enzymes, antibodies and nucleic acids) widely used in optical-based biosensors. Varying factors which include specificity, selectivity, reusability, reaction time, detection threshold, long-term stability and cost of production were identified to have significantly impacted their adoption in optical-based biosensors. These factors have also necessitated their preference and integration in portable designs as point-of-use devices for food monitoring. This review evaluates the strengths, limitations and immobilization technologies of these bioreceptors. From the reviewed studies, incorporating artificial intelligence and machine learning into optical biosensors has significant potential to ensure a more precise food safety monitoring system. While the reviewed studies have indicated the prospects of bioreceptors in food safety monitoring, in-depth studies are required to harness and scale up their practical applications as optical biosensor elements in food supply systems. [ABSTRACT FROM AUTHOR]

Copyright of Discover Sensors is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)