通过机器学习增强的振动光谱技术,推进对食品中的农药残留物的智能检测
Siyu Yao1, Tong Yu1, Alessandra Fantina Victorio Ramos2
1Key Laboratory of Environmental Medicine and Engineering, Ministry of Education, and Department of Nutrition and Food Hygiene, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, China.
Food chemistry
|December 17, 2025
概括
智能技术正在彻底改变食品中的农药残留物检测. 振动光谱学与机器学习相结合,提供了快速,非破坏性和具有成本效益的食品安全解决方案.
科学领域:
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
- 频谱学是一种光谱学.
背景情况:
- 全球农药使用需要强有力的食品安全监测.
- 传统的检测方法 (HPLC,GC-MS) 是耗时且设备密集的.
- 迫切需要快速,非破坏性,且具有成本效益的农药残留分析.
研究的目的:
- 审查食品中农药残留物的智能检测方面的进展.
- 强调振动光谱 (MIR,NIR,Raman,SERs) 与机器学习 (ML) 的整合.
- 探索提高农药检测可访问性和效率的创新.
主要方法:
- 使用振动光谱技术:中红外 (MIR),近红外 (NIR),拉曼和表面增强的拉曼光谱 (SER).
- 整合机器学习 (ML) 用于自动化光谱分析和解释.
- 利用纳米材料,智能手机集成,人工智能和微型化方面的创新.
主要成果:
- ML显著提高了振动光谱的诊断能力.
- 创新提高了杀虫剂检测的可访问性,精度和运营效率.
- 发展使得可以更快,更准确地选农药残留物.
结论:
- 智能检测技术为农药残留查提供了更高的准确性和速度.
- 综合系统为便携式,用户友好的食品安全解决方案铺平了道路.
- 这些进展对于提高食品安全和保护公共健康至关重要.
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