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Outline
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.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 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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