SERS协同机器学习在生物医药检测中的应用

doi:10.6040/j.issn.1671-9352.0.2025.116

摘要/Abstract

摘要： 表面增强拉曼光谱(surface-enhanced Raman spectroscopy, SERS)能够捕获分子的化学键振动特征,实现高灵敏度和特异性的无标记检测,在检测生物样品方面具有显著的优势和应用潜力,但面临数据特征复杂、光谱波动性、重复性、特征峰重叠等挑战。机器学习(machine learning, ML)通过算法和模型使计算机从数据中自动学习并进行预测或决策,在解析生物样品的复杂SERS光谱,推进其临床应用中展现出广阔前景。本文综述ML算法如何提升SERS性能,介绍用于ML的SERS数据的预处理方法、探讨ML处理SERS数据的功能类型和基本流程,并重点介绍其在分类和定量分析以及疾病诊断中的应用。此外,本文还讨论ML辅助SERS在分子结构预测、拉曼光谱数据库构建及DNA与RNA区分方面的潜力。最后,对ML集成SERS的挑战和未来发展方向进行展望。

关键词: 表面增强拉曼散射, 机器学习, 生物医药, 特征提取, 疾病诊断

Abstract: Surface-enhanced Raman spectroscopy(SERS)enables highly sensitive and specific label-free detection by capturing the vibrational signatures of molecular chemical bonds. It offers significant advantages and broad application potential in the analysis of biological samples. However, its clinical translation remains limited due to challenges such as spectral complexity, variability, limited reproducibility, and overlapping characteristic peaks. Machine learning(ML), which allows computational models to learn patterns from data and make informed predictions or decisions, has shown great promise in deciphering complex SERS spectra and advancing their biomedical applications. This review highlights recent advances in the integration of ML with SERS, detailing how ML algorithms enhanced analytical performance. It also outlines common data preprocessing techniques for SERS, describes core ML workflows, and examines their roles in classification, quantitative analysis, and disease diagnosis. Furthermore, the review explores emerging applications such as molecular structure prediction, Raman spectral database development, and discrimination between DNA and RNA. Finally, the current challenges and future directions of ML-assisted SERS are discussed, with an emphasis on improving robustness, interpretability, and clinical applicability.

Key words: SERS, machine learning, biomedicine, feature extraction, disease diagnosis

中图分类号:

O657.37

陈云帆,王也晨,王龙,安琪,冯泽国. SERS协同机器学习在生物医药检测中的应用[J]. 《山东大学学报(理学版)》, 2025, 60(10): 23-41.

CHEN Yunfan, WANG Yechen, WANG Long, AN Qi, FENG Zeguo. Application of SERS collaborative machine learning in biomedical detection[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2025, 60(10): 23-41.

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