机器学习 (ML) 辅助的表面增强雷曼光谱 (SERS) 技术用于可持续的健康
Kamil Reza Khondakar1, Hirak Mazumdar2, Suparna Das3
1School of Technology, India and Woxsen University, Hyderabad 502345, Telangana, India.
Advances in colloid and interface science
|July 15, 2025
概括
表面增强拉曼光谱 (SERS) 与机器学习 (ML) 结合,提供了增强的分子诊断和环境监测. 这种集成使得人工智能驱动的复杂SERS数据的解释能够实现更准确和更智能的传感应用.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 数据科学数据科学数据科学
背景情况:
- 表面增强拉曼光谱 (SERS) 提供了超高灵敏度的无标签检测.
- 在医疗保健,食品安全和环境监测方面应用SERS.
- 从复杂的混合物中分析大型SERS数据集对准确的分析物鉴定提出了重大挑战.
研究的目的:
- 审查机器学习 (ML) 与SERS的整合.
- 探索ML算法如何增强SERS功能,以提高准确性和洞察力.
- 要突出SERS从简单传感到通过AI/ML智能传感的过渡.
主要方法:
- 将ML算法和多变量统计分析与SERS数据采集相结合.
- 利用AI/ML进行快速分析和自动处理SERS光谱数据.
- 审查有关ML-SERS集成和应用的现有文献.
主要成果:
- 机器学习算法显著改善了从复杂的SERS数据集中提取和预测结果.
- AI/ML促进了自动化数据处理和SERS签名的解释.
- ML和SERS的协同作用使得分子诊断和查更准确.
结论:
- 机器学习与SERS的整合将这项技术从基本检测转变为智能传感.
- 人工智能/ML增强了SERS分析,为诊断和监测提供了更准确和更有洞察力的结果.
- 未来的ML增强的SERS传感器技术对各种科学领域具有变革潜力.
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