Recent Advances in Raman Spectral Classification with Machine Learning.

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Data Science

Background:

• Raman spectroscopy offers non-destructive molecular analysis but faces challenges with weak signals and complex data.
• Traditional chemometric methods struggle with nonlinear Raman data and require manual feature engineering.

Purpose of the Study:

• To review research progress, trends, and future directions in Machine Learning (ML)-assisted Raman spectral classification.
• To provide a comprehensive overview of ML and Deep Learning (DL) applications in Raman spectral analysis.

Main Methods:

• Structured narrative review methodology.
• Analysis of traditional ML models and advanced DL architectures for Raman spectral data.
• Identification of key application domains.

Main Results:

• ML and DL enable automatic feature learning from raw Raman data, overcoming limitations of traditional methods.
• ML-assisted Raman spectroscopy shows promise in biomedical diagnostics, food safety, mineralogy, and plastic identification.
• Review highlights current trends and applications of ML/DL in Raman spectral classification.

Conclusions:

• ML and DL fusion offers a robust solution for complex Raman spectral interpretation and classification.
• Despite advancements, challenges like limited data, generalization, reproducibility, and interpretability persist.
• Future research should address these challenges to further advance ML-assisted Raman spectroscopy.