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Design\u002Fmethodology\u002Fapproach A total of 2,782 documents published between 2021 and 2025 were retrieved from the Web of Science Core Collection. The dataset was refined by removing duplicates, filtering document types and excluding irrelevant records. VOSviewer was then used to conduct co-authorship, keyword co-occurrence, citation, co-citation and bibliographic coupling analyses. Findings Machine learning and deep learning emerged as core technologies associated with food security, contamination detection and quality prediction. Keyword clustering demonstrated interdisciplinary connections with agriculture, climate change and public health. Citation and co-citation analyses identified three major intellectual foundations: traditional machine learning, computer vision-based deep learning and time-series modeling. China, India and the United States were the most influential contributors in terms of research output and technological development. Bibliographic coupling and source co-citation analyses further indicated increasing convergence between food science and computational science. Originality\u002Fvalue This study provides a comprehensive overview of the intellectual structure, collaborative networks and thematic evolution of AI-driven food safety research. Unlike previous studies focusing on specific technologies or applications, it maps global research patterns and interdisciplinary linkages. The findings provide strategic insights for researchers, policymakers and industry stakeholders. Future studies should prioritize explainable AI integration into regulatory frameworks and the development of larger, more diverse datasets to improve model generalizability across food systems.","目的 本研究旨在通过全面的文献计量分析，识别人工智能（AI）与食品安全及保障融合领域的最新研究趋势。设计与方法 从Web of Science核心合集中检索了2021年至2025年间发表的2，782篇文献。通过去除重复记录、筛选文献类型并排除不相关记录对数据集进行精炼。随后使用VOSviewer进行合著、关键词共现、引文、共被引及文献耦合分析。研究发现 机器学习和深度学习成为与食品安全、污染检测和质量预测相关的核心技术。关键词聚类显示了与农业、气候变化和公共卫生的跨学科联系。引文和共被引分析识别出三大主要知识基础：传统机器学习、基于计算机视觉的深度学习和时间序列建模。中国、印度和美国在研究产出和技术发展方面是最具影响力的贡献者。文献耦合和来源共被引分析进一步表明食品科学与计算科学之间的融合日益加深。原创性\u002F价值 本研究全面概述了AI驱动的食品安全研究的知识结构、合作网络和主题演变。与以往聚焦特定技术或应用的研究不同，本研究绘制了全球研究模式和跨学科联系。研究结果为研究人员、政策制定者和行业利益相关者提供了战略性见解。未来研究应优先关注可解释AI融入监管框架以及开发更大、更多样化的数据集，以提高模型在食品系统中的泛化能力。","British Food Journal","2026-10-19T00:00:00Z","论文",false,80,{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":173,"relevant":455,"comment":584},"基于2782篇文献的AI食品安全计量分析，方法规范、结论有参考价值，但属综述性研究，产业落地影响有限。",[586],{"name":578,"url":575},[588,589,590,591,592],"智慧农业","农业人工智能","机器学习","食品安全","文献计量",{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},[595],{"code":465,"slug":466,"name":467,"path":468,"parent_code":434},{"status":597,"locked":581,"version":4,"method":598,"evidence":599},"classified","rules",{"candidates":600,"agricultural_evidence":603},[601,613,619,624,631,637],{"code":24,"score":602,"supported":603,"title":604,"abstract":606,"tags":612},19,true,[605],"artificial intelligence",[607,590,608,609,610,605,611],"深度学习","计算机视觉","machine learning","deep learning","computer vision",[589,590],{"code":465,"score":614,"supported":603,"title":615,"abstract":617,"tags":618},15,[616],"food safety",[591,616],[591],{"code":64,"score":614,"supported":603,"title":620,"abstract":622,"tags":623},[621],"bibliometric",[592,621],[592],{"code":184,"score":625,"supported":603,"title":626,"abstract":627,"tags":630},6,[],[628,629],"气候变化","climate change",[],{"code":263,"score":632,"supported":603,"title":633,"abstract":634,"tags":636},3,[],[635],"food security",[],{"code":16,"score":455,"supported":581,"title":638,"abstract":639,"tags":640},[],[],[588],[642,643],"AI 食品安全 文献计量","机器学习 食品质量预测","AI食品安全文献计量-5400","10.1108\u002Fbfj-06-2025-0819",{"doi":645,"openalex_id":647,"authors":648,"venue":578,"cited_by_count":424,"oa_url":11,"card":662,"direction":666,"ingested_from":668},"W7222920742",[649,651,653,656,659],{"name":650,"orcid":11},"Harang Lee",{"name":652,"orcid":11},"지은서",{"name":654,"orcid":655},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":657,"orcid":658},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":660,"orcid":661},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":663,"method":664,"finding":665,"direction":666,"opportunity":667},"对2021-2025年2782篇文献做文献计量，梳理AI与食品安全交叉研究趋势。","Web of Science文献计量，VOSviewer共被引、关键词共现与耦合","机器学习与深度学习是核心，形成传统ML、计算机视觉、时序建模三大知识基础。","农业人工智能与决策模型","可解释AI嵌入监管框架、构建更大更多样数据集以提升跨食物系统泛化能力。","openalex","2026-10-20T23:30:31.567802Z",{"technologies":671,"keywords":678},[672,673,675,676],{"name":589,"count":455},{"name":674,"count":455},"区块链",{"name":590,"count":455},{"name":677,"count":455},"遥感监测",[679,680,681,683,685,687,689,691,693,695,697,699],{"name":591,"count":173},{"name":588,"count":400},{"name":682,"count":69},"小农户",{"name":684,"count":69},"水产养殖",{"name":686,"count":455},"农业可持续",{"name":688,"count":455},"农业数字化转型",{"name":690,"count":455},"农产品出口",{"name":692,"count":455},"农产品加工",{"name":694,"count":455},"农产品质量安全",{"name":696,"count":455},"农药残留",{"name":698,"count":455},"可信数据空间",{"name":700,"count":455},"土壤修复"]