Bibliometric analysis of MOF-based fluorescence detection in food safety: Development, multi-strategy optimization, and future AI integration

The escalating incidence of foodborne diseases challenges global public health. Metal-organic framework (MOF)-based fluorescence detection offers a robust solution for food safety assurance, leveraging advantages of high sensitivity, rapid response, and operational simplicity. Although approximately 800 related publications have emerged in the past decade, comprehensive bibliometric analysis remains absent. This study, CiteSpace-based bibliometric was adopted to quantitatively analyze and visualize publications, with the objective of comprehensively assess technological developments in food safety and exploring current progress alongside key future trends. This study highlights that the sensitivity, selectivity, and efficiency of fluorescence detection can be significantly improved by modulating MOF structures and integrating artificial intelligence (AI) for data analysis and pattern recognition. These improvements facilitate the development of high-performance MOF with tailored structures, stable and eco-friendly. This work provides innovative directions for the development of intelligent food safety monitoring systems by promoting the synergistic integration of MOF-based fluorescence detection with AI.