Machine Learning-Enabled Intelligent Analysis of Surface-Enhanced Raman Scattering: Methods, Applications, and...

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Data Science

Background:

• Surface-enhanced Raman spectroscopy (SERS) offers ultrasensitive molecular detection.
• SERS generates high-dimensional, substrate-dependent data challenging conventional analysis.
• Machine learning (ML) integration presents opportunities for SERS data interpretation and substrate optimization.

Purpose of the Study:

• To review recent advances in ML-assisted SERS across the analytical workflow.
• To discuss ML approaches for SERS spectral analysis and substrate design.
• To highlight challenges and future directions in ML-SERS integration.

Main Methods:

• Overview of data characteristics and preprocessing strategies for SERS.
• Exploration of supervised, unsupervised, and deep learning for spectral classification and quantification.
• Discussion of ML-driven substrate optimization techniques like surrogate modeling and inverse design.

Main Results:

• ML facilitates chemical information extraction from complex SERS datasets.
• ML aids in optimizing nanostructured substrates for enhanced SERS signals.
• Applications include biomarker discovery and spectral fingerprint recognition with a focus on interpretability.

Conclusions:

• The convergence of ML and SERS is transforming Raman-based analysis towards predictive and integrated sensing.
• ML-driven strategies are emerging for efficient SERS substrate design.
• Addressing challenges like data scarcity and generalization is crucial for real-time SERS deployment.