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Outline
Food safety issues have garnered increasing global attention, creating an urgent need for the development of rapid, accurate and on-site detection technology, and the integration of intelligent and rapid detection methods provides a crucial direction for future development. This paper aimed to systematically review the latest advances in the integration of artificial intelligence (AI) and rapid food detection technology. Using this as the core analytical perspective, it compared and examined the different roles, integration modes and driving logics of such integration in the technological development paths of China and major developed countries. A comparison of development paths between China and other countries revealed that China had established a rapid screening system suitable for grassroots regulatory use through technologies like colloidal gold multi-test card. In contrast, developed countries had focused more on high-precision biosensing technologies and established comprehensive precision detection networks that cover the entire industry chain. Rapid detection technologies in China currently faced challenges, including incomplete maturation of core material preparation processes and the operational proficiency of grassroots testing personnel not meeting the requirements of intelligent equipment. In the future, the direction of multi-source and multi-element fusion would become one of the important development trends, which was mainly reflected in the in-depth integration of AI with biosensors. For instance, the coupling of Raman spectroscopy with deep learning could not only achieve rapid and accurate detection of complex food matrices, but also derived the evolution of detection systems from a single-purpose tool into intelligent decision-making systems. Furthermore, this paper proposed that to enhance the grasp of the development trajectory of intelligent food rapid detection technology and improve rove China’s technology research and development layout, it was important to strengthen international cooperation and mutual recognition of detection standards. Active participation in the International Technical Alliance for Food Detection was essential to contribute greater efforts toward advancing the global food safety governance system.
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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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