Yin Binfeng, Tan Gang, Muhammad Rashid, Liu Jun, Bi Junjie
School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.
Suqian Product Quality Supervision and Inspection Institute, Suqian 223800, China.
Foods. 2025 Jun 2;14(11):1973. doi: 10.3390/foods14111973.
Artificial intelligence is comprehensively transforming the food safety governance system by integrating modern technologies and building intelligent control systems that provide rapid solutions for the entire food supply chain from farm to fork. This article systematically reviews the core applications of AI in the orbit of food safety. First, in the production and quality control of primary food sources, the integration of spectral data with AI efficiently identifies pest and disease, food spoilage, and pesticide and veterinary drug residues. Secondly, during food processing, sensors combined with machine learning algorithms are utilized to ensure regulatory compliance and monitor production parameters. AI also works together with blockchain to build an immutable and end-point traceability system. Furthermore, multi-source data fusion can provide personalized nutrition and dietary recommendations. The integration of AI technologies with traditional food detection methods has significantly improved the accuracy and sensitivity of food analytical methods. Finally, in the future, to address the increasing food safety issues, Food Industry 4.0 will expand the application of AI with lightweight edge computing, multi-modal large models, and global data sharing to create a more intelligent, adaptive and flexible food safety system.
人工智能正在通过整合现代技术和构建智能控制系统,全面变革食品安全治理体系,这些智能控制系统为从农场到餐桌的整个食品供应链提供快速解决方案。本文系统回顾了人工智能在食品安全领域的核心应用。首先,在初级食品来源的生产和质量控制方面,光谱数据与人工智能的整合能够高效识别病虫害、食品变质以及农药和兽药残留。其次,在食品加工过程中,传感器与机器学习算法相结合,用于确保符合监管要求并监测生产参数。人工智能还与区块链协同工作,构建一个不可篡改的端点可追溯系统。此外,多源数据融合可以提供个性化的营养和饮食建议。人工智能技术与传统食品检测方法的结合显著提高了食品分析方法的准确性和灵敏度。最后,未来为应对日益增加的食品安全问题,食品工业4.0将通过轻量级边缘计算、多模态大模型和全球数据共享来扩大人工智能的应用,以创建一个更智能、自适应和灵活的食品安全系统。