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<p>Surface-enhanced Raman scattering (SERS) is an optical analytical technique that enables the detection of specific molecules with high sensitivity via plasmonic effect of metal nanostructures. Despite its advantages in sensing various biomolecules, the difficulties in establishing reliable SERS-active substrates, as well as the complexity of interpreting SERS spectra, hinder the practical applications of SERS-based biosensors in the biomedical field. Recent advancements in machine learning (ML) technology have facilitated data analysis, thereby reducing these limitations of SERS-based biosensors. In this review article, the introduction of ML in the development of SERS biosensors for diagnostic platforms will be discussed. Firstly, a brief overview of the ML algorithm used in the SERS study is introduced. Two main applications of ML in SERS biosensors, ML-based design of novel SERS-active nanostructures and ML-assisted data analysis of SERS signals, will be described next, and the future perspectives and challenges of ML-integrated SERS sensors in the biomedical field will be presented.</p>

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Integration of Machine Learning in Surface-Enhanced Raman Spectroscopy Biosensor for Biomedical Applications

  • 期刊论文

  • 发表日期：

    2025年8月18日

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  • Jong Uk Lee,
  • Hye Jin Kim

摘要

Surface-enhanced Raman scattering (SERS) is an optical analytical technique that enables the detection of specific molecules with high sensitivity via plasmonic effect of metal nanostructures. Despite its advantages in sensing various biomolecules, the difficulties in establishing reliable SERS-active substrates, as well as the complexity of interpreting SERS spectra, hinder the practical applications of SERS-based biosensors in the biomedical field. Recent advancements in machine learning (ML) technology have facilitated data analysis, thereby reducing these limitations of SERS-based biosensors. In this review article, the introduction of ML in the development of SERS biosensors for diagnostic platforms will be discussed. Firstly, a brief overview of the ML algorithm used in the SERS study is introduced. Two main applications of ML in SERS biosensors, ML-based design of novel SERS-active nanostructures and ML-assisted data analysis of SERS signals, will be described next, and the future perspectives and challenges of ML-integrated SERS sensors in the biomedical field will be presented.