基于SERS的兽医药物检测新兴趋势:多功能基板和智能数据方法
Tianzhen Yin1, Yankun Peng2, Kuanglin Chao3
1National R & D Center for Agro-processing Equipment, College of Engineering, China Agricultural University, Beijing, China.
NPJ science of food
|March 16, 2025
概括
本综述探讨了表面增强的拉曼散射 (SERS) 用于检测食品中的兽药残留物. 它强调了SERS基板的进步和深度学习的整合,以改善食品安全分析.
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:
- 食品中的兽药残留物对食品安全构成重大挑战.
- 表面增强拉曼散射 (SERS) 为残留物检测提供了高灵敏度和特异性.
- 开发可靠的SERS基板和解释光谱数据仍然是关键障碍.
研究的目的:
- 审查用于兽医药物检测的SERS基质的发展.
- 在基于SERS的分析中探索深度学习的整合.
- 确定局限性,并提出提高食品安全的未来方向.
主要方法:
- SERS基板的分类 (基于金属的,刚性,灵活,多功能).
- 讨论应用场景和检测要求.
- 集成深度学习用于基板设计,光谱分析和数据解释.
主要成果:
- 综述各种SERS基质类型及其在兽医药物检测中的应用.
- 展示深度学习在优化SERS性能和数据分析方面的潜力.
- 确定包括记者分子选择和计算需求在内的挑战.
结论:
- 特别是在与深度学习相结合时,SERS对敏感和特定的兽药残留物检测具有很大的前景.
- 需要进一步的研究来克服基板开发和数据处理的局限性,以实现强大的食品安全应用.
- 未来的趋势指向多功能基板和先进的计算方法,以提高分析能力.
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