School of Marine Science and Technology, Harbin Institute of Technology (Weihai), Weihai 264209, China.
CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Shandong Key Laboratory of Coastal Environmental Processes, Shandong Research Center for Coastal Environmental Engineering and Technology, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China.
Analyst. 2023 Aug 21;148(17):3971-3985. doi: 10.1039/d3an00483j.
Environmental protection and food safety are closely related to the healthy development of human society; there is an urgent need for relevant analytical methods to determine environmental pollutants and harmful substances in food. Molecular imprinting-based ratiometric fluorescence (MI-RFL) sensors, constructed by combining molecular imprinting recognition and ratiometric fluorescence detection, possess remarkable advantages such as high selectivity, anti-interference ability, high sensitivity, non-destruction and convenience, and have attracted increasing interest in the field of analytical determination. Herein, recent advances in MI-RFL sensors for environmental and food analysis are reviewed, aiming at new construction strategies and representative determination applications. Firstly, fluorescence sources and possible sensing principles are briefly outlined. Secondly, new imprinting techniques and dual/ternary-emission fluorescence types that improve sensing performances are highlighted. Thirdly, typical analytical applications of MI-RFL sensors in environmental and food samples are summarized. Lastly, the challenges and perspectives of the MI-RFL sensors are proposed, focusing on improving sensitivity/visualization and extending applications.
环境保护和食品安全与人类社会的健康发展息息相关；迫切需要相关的分析方法来测定食品中的环境污染物和有害物质。基于分子印迹的比率荧光（MI-RFL）传感器通过结合分子印迹识别和比率荧光检测构建，具有高选择性、抗干扰能力、高灵敏度、非破坏性和便利性等显著优势，在分析测定领域引起了越来越多的关注。本文综述了用于环境和食品分析的 MI-RFL 传感器的最新进展，旨在介绍新的构建策略和有代表性的测定应用。首先，简要概述了荧光源和可能的传感原理。其次，重点介绍了提高传感性能的新型印迹技术和双/三元发射荧光类型。然后，总结了 MI-RFL 传感器在环境和食品样品中的典型分析应用。最后，提出了 MI-RFL 传感器的挑战和展望，重点是提高灵敏度/可视化程度和拓展应用。
