Application and Prospect of Integrated Food Testing Technology Based on Artificial Intelligence

被引:0
|
作者
Ding, Haohan [1 ,2 ]
Xie, Zhenqi [2 ]
Shen, Song [2 ]
Cui, Xiaohui [1 ,3 ]
Wang, Zhenyu [4 ]
Fu, Ou [1 ]
Wan, Jun [2 ]
机构
[1] Science Center for Future Foods, Jiangnan University, Wuxi,214122, China
[2] School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi,214122, China
[3] School of Cyber Science and Engineering, Wuhan University, Wuhan,430072, China
[4] Jiaxing Institute of Future Food, Jiaxing,314005, China
来源
关键词
Authentication - Blockchain - Deep learning - Food safety;
D O I
10.12301/spxb202400408
中图分类号
学科分类号
摘要
The integration of artificial intelligence (AI) and rapid detection technologies has brought significant transformations to the field of food testing, driving advancements in food safety, quality, and authenticity identification capabilities. The current status of the application of integrated rapid detection technologies in food testing technology based on AI was introduced. In chemical and biological contamination testing, the combination of AI and rapid testing technologies effectively enhances the efficiency and accuracy of food safety testing. In terms of food quality testing, AI combined with rapid testing technologies is widely used for quality assessment of fruits, meat products, aquatic products, and other foods, achieving precise measurement through efficient data analysis and intelligent recognition. Moreover, AI is applied in food authenticity identification, including adulteration detection and geographic traceability, ensuring the authenticity and reliability of food products. Especially when blockchain technology is integrated with AI, blockchain-based traceability systems ensure full traceability of food through transparent and tamper-proof records, while AI algorithms can analyze data from the food supply chain in real-time to identify potential risks and anomalies. The integration of these technologies could not only enhance the overall level of food testing, but also lay the foundation for the construction of future intelligent food testing systems. © 2024 Beijing Technology and Business University, Department of Science and Technology. All rights reserved.
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页码:13 / 23
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