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

Mao Zhang, Yufang He, Zili Tang, Caiyi Qin, Lei Zhang, Hongdong Liu

Abstract readReview

In one paragraph

Review in Food chemistry: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mao ZhangChongqing Key Laboratory of Conservation and Utilization of Freshwater Fishes, Animal Biology Key Laboratory of Chongqing Education Commission of China, College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China.

Yufang HeChongqing Key Laboratory of Conservation and Utilization of Freshwater Fishes, Animal Biology Key Laboratory of Chongqing Education Commission of China, College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China.

Zili TangChongqing Key Laboratory of Conservation and Utilization of Freshwater Fishes, Animal Biology Key Laboratory of Chongqing Education Commission of China, College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China.

Caiyi QinChongqing Key Laboratory of Conservation and Utilization of Freshwater Fishes, Animal Biology Key Laboratory of Chongqing Education Commission of China, College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China.

Lei ZhangChongqing Key Laboratory of Conservation and Utilization of Freshwater Fishes, Animal Biology Key Laboratory of Chongqing Education Commission of China, College of Life Sciences, Chongqing Normal University, Chongqing 401331, PR China.

Hongdong LiuSchool of Energy Chemical Industry and Environment, Sichuan Vocational and Technical College, Suining, Sichuan 629000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AI fusionCiteSpaceFluorescence detectionFood safetyMetal-organic frameworkMulti-strategy optimization

Identifiers

PMID41438543

DOI10.1016/j.fochx.2025.103331

PMCPMC12719190

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.