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Advances in smartphone assisted sensors for on-site detection of food safety based on fluorescence on-off-on mode: A review

背景(考古学) 小型化 纳米技术 过程(计算) 计算机科学 系统工程 工程类 实时计算 材料科学 操作系统 生物 古生物学
作者
Wei Fu,Xiaqing Fu,Zhemin Li,Zhifang Liu,Xian Li
出处
期刊:Chemical Engineering Journal [Elsevier BV]
卷期号:489: 151225-151225 被引量:19
标识
DOI:10.1016/j.cej.2024.151225
摘要

As technology continues to advance and new application scenarios emerge, accurate and rapid on-site detection sensors are essential for monitoring and control the quality and safety of food. The excellent photography and image processing capabilities of smartphone has greatly facilitated the miniaturization, integration, and visual monitoring of sensors, making them essential tools for portable real-time determination. The "on–off-on" fluorescence sensor has garnered significant attention due to its remarkable features, including high sensitivity, excellent selectivity, strong anti-interference ability, and customizable probe structure. In this context, portable sensors with intelligent analysis and sensing systems have been extensively reported according to the "on–off-on" fluorescent signals, and highlight their immense potential and value in various fields. However, to date, a comprehensive review of smartphone-based on-site detection platforms driven by the "on–off-on" fluorescence mechanism remains absent. This review aims to fill this gap by providing an elaborate explanation of the "on–off-on" fluorescence response process, along with a detailed introduction to commonly used fluorescent materials and their underlying design principles. Furthermore, the latest applications of portable sensors based on the "on–off-on" fluorescence approach in detection of food safety are thoroughly reviewed. Finally, an in-depth analysis was conducted on the challenges and future development directions of the smartphone based sensing systems. This review aims to provide a valuable theoretical basis for comprehending and designing portable sensors, ultimately contributing to more effective and accurate real-time detection of pollutants.
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