Application and Machine Learning‐Assisted Recognition of MOF‐Based Fluorescent Sensors in the Detection of Food Contaminants

ABSTRACT Fluorescence sensing technology plays a crucial role in environmental monitoring and food safety, resulting from its remarkable sensitivity and visualization capabilities. Metal‐organic frameworks (MOFs) have emerged as ideal platforms for high‐performance fluorescent probes, mainly due to their large specific surface area, adjustable luminescent properties, and diverse structures. This review summarizes the recent research progress of metal‐organic frameworks (MOFs) and their composites in the field of fluorescence sensing, focusing on elaborating their luminescence mechanisms and sensing mechanisms, systematically introduces the detection of ions (such as Al 3 ⁺, Cu 2 ⁺, Cr 2 O 7 2− , etc.) and organic pollutants in foods, and highlights the advantages of the combination of machine learning algorithms and MOFs. Furthermore, the current challenges in this field are carefully analyzed, and future research directions are proposed, which thus offer a basic reference for promoting the development of MOF‐based fluorescence sensing technologies.