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Research progress on the application potential of artificial intelligence in the analysis of emerging contaminants in food
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Jin-Hua WANG, Rong ZHANG, Xin FENG, Wei BIE, Zhen LI, Xi-Cheng ZHOU, Xiao-Long ZHANG*
Journal of Food Safety & Quality | 2026, 17(8) : 3 - 11
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Journal of Food Safety & Quality | 2026, 17(8): 3-11
• Highlight: Science and Technology Research Center of China Customs •
Research progress on the application potential of artificial intelligence in the analysis of emerging contaminants in food
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Jin-Hua WANG, Rong ZHANG, Xin FENG, Wei BIE, Zhen LI, Xi-Cheng ZHOU, Xiao-Long ZHANG*
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  • Science and Technology Research Center of China Customs, Bejing 100026, China
doi: 10.19812/j.cnki.jfsq11-5956/ts.20251230001
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Persistent organic pollutants (POPs), microplastics (MPs), endocrine disruptors, antibiotics and other emerging contaminants (ECs) are currently major risk factors threatening food safety and harming public health.Traditional food safety analytical methods face technical bottlenecks in addressing unknown and complex new contaminants. In recent years, artificial intelligence (AI) technologies, such as machine learning (ML), have provided a novel and powerful tool to tackle this challenge due to their strong data processing capabilities and excellent pattern recognition performance. This article elaborated on the prominent advantages of AI technology in optimizing sample pre-treatment processes, improving instrumental analysis efficiency, constructing screening databases and extracting confirmatory information in key analytical processes, and assessed its potential applications in the analysis of emerging contaminants in food. Despite broad prospects, this field still faced challenges related to data quality, model interpretability, standardisation and institutional and procedural mechanisms. Overall, AI technology is effectively promoting the development of intelligent and predictive analysis of emerging contaminants in food.

food safety  /  artificial intelligence  /  emerging contaminants  /  detection and analysis
王金花, 张蓉, 冯鑫, 别玮, 李祯, 周熙成, 张晓龙. 人工智能在食品新污染物分析中的应用潜力研究进展. 食品安全质量检测学报, 2026 , 17 (8) : 3 -11 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20251230001
Jin-Hua WANG, Rong ZHANG, Xin FENG, Wei BIE, Zhen LI, Xi-Cheng ZHOU, Xiao-Long ZHANG. Research progress on the application potential of artificial intelligence in the analysis of emerging contaminants in food[J]. Journal of Food Safety & Quality, 2026 , 17 (8) : 3 -11 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20251230001
Year 2026 volume 17 Issue 8
8
1
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20251230001
  • Receive Date:2025-12-30
  • Online Date:2026-11-05
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  • Received:2025-12-30
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    Science and Technology Research Center of China Customs, Bejing 100026, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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