Deep learning-empowered SERS: deciphering the multidimensional information code of complex biological samples.

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Artificial Intelligence

Background:

• Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive analytical technique with broad applications in biomedicine, environmental monitoring, and food safety.
• Spectral noise in SERS detection reduces the signal-to-noise ratio (SNR), hindering accurate sample analysis and limiting its use in complex analytical chemistry domains.
• Deep learning (DL) excels at pattern recognition and representation learning, offering potential solutions for data complexity and noise reduction.

Purpose of the Study:

• To review recent advancements in integrating deep learning with surface-enhanced Raman scattering technology.
• To provide insights into the challenges and future prospects of DL-enhanced SERS.
• To promote the development of SERS technology in complex analytical applications.

Main Methods:

• Literature review of studies combining deep learning algorithms with surface-enhanced Raman spectroscopy.
• Analysis of DL approaches for spectral denoising and signal enhancement in SERS data.
• Exploration of AI-driven methodologies for improving SERS accuracy and reliability.

Main Results:

• Deep learning effectively addresses spectral noise issues in SERS, significantly improving the signal-to-noise ratio.
• AI integration enables SERS to handle complex datasets and influencing factors, enhancing analytical performance.
• Recent DL models demonstrate autonomous learning capabilities for high-level feature extraction in SERS.

Conclusions:

• The integration of deep learning with SERS technology is crucial for overcoming spectral noise limitations.
• DL-enhanced SERS offers a promising pathway for more accurate and reliable analysis in diverse scientific fields.
• Future research should focus on developing advanced AI algorithms to further propel SERS capabilities.