Surface Enhanced Raman Scattering for Biomolecular Sensing in Human Healthcare Monitoring

Area of Science:

• Analytical Chemistry
• Biotechnology
• Spectroscopy

Background:

• Surface-enhanced Raman scattering (SERS) has enabled sensitive biomolecule detection since the 1980s.
• Challenges like reproducibility and scalability have limited SERS adoption.
• Portable Raman spectrometers are enabling new point-of-use (POU) SERS applications.

Purpose of the Study:

• To review advancements in SERS for point-of-use (POU) testing.
• To explore the integration of machine learning with SERS for data analysis.
• To highlight the potential of this combined platform for human healthcare monitoring.

Main Methods:

• Review of literature on SERS applications in POU platforms (e.g., lateral flow assays, wearable sensors, VOC detection).
• Discussion of machine learning algorithms for SERS data interpretation.
• Analysis of SERS performance against gold standard methods.

Main Results:

• SERS shows promise in POU platforms for healthcare monitoring.
• Machine learning provides rapid and accurate analysis of SERS data.
• Favorable results rivaling gold standard approaches have been reported.

Conclusions:

• The combination of SERS with POU testing and machine learning presents a powerful platform.
• This integrated approach addresses limitations of traditional SERS.
• It holds significant potential for sensitive detection and discrimination of clinical biomolecules for enhanced human healthcare.