Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review

被引:23
作者
Dhal, Sambandh Bhusan [1 ]
Kar, Debashish [2 ]
机构
[1] US DOE, Dept Analyt Chem, Idaho Natl Lab, Directorate Energy Environm Sci & Technol, Idaho Falls, ID 83415 USA
[2] Texas A&M AgriLife Res, College Stn, TX 77840 USA
关键词
Artificial intelligence; machine learning; deep learning; natural language processing; computer vision; Internet of Things; blockchain; food safety; food quality; food security; COMPUTER VISION; BIG DATA; RISK IDENTIFICATION; LEARNING TECHNIQUES; FUTURE CHALLENGES; DECISION-MAKING; INTERNET; MANAGEMENT; INSPECTION; THINGS;
D O I
10.1007/s42452-025-06472-w
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust.
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页数:46
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