Current trends in machine learning for surface-enhanced Raman spectroscopy

Area of Science:

• Surface-enhanced Raman spectroscopy (SERS)
• Artificial Intelligence (AI) in Spectroscopy

Background:

• SERS analysis is increasingly integrated with AI across its methodological spectrum.
• Conventional machine learning and advanced deep learning architectures are key to SERS advancements.

Purpose of the Study:

• To review the current AI methodologies transforming SERS analysis.
• To outline practical guidelines and a future path for AI in SERS.

Main Methods:

• Application of conventional machine learning for baseline correction and deployment.
• Utilization of deep learning (CNNs, RNNs, Transformers) for spectral representation learning.
• Employing generative models (GANs, VAEs, Diffusion) for data augmentation and denoising.
• Leveraging large language models for metadata curation and decision support.

Main Results:

• AI enhances SERS convenience, scalability, and automation for applications in medicine, agriculture, food, environment, and process control.
• Significant progress in spectral representation learning and data augmentation through AI.
• AI facilitates metadata curation and retrieval-augmented decision support in SERS.

Conclusions:

• Despite AI advancements, challenges in data scarcity and model explainability persist.
• Future directions emphasize FAIR data principles, transparent evaluation, and interpretable, uncertainty-aware AI models.
• Community-driven efforts are crucial for robust and reliable AI-powered SERS deployment across diverse settings.