Surface Enhanced Raman Scattering for Biomolecular Sensing in Human Healthcare Monitoring

Since the 1980s, surface enhanced Raman scattering (SERS) has been used for the rapid and sensitive detection of biomolecules. Whether a label-free or labeled assay is adopted, SERS has demonstrated low limits of detection in a variety of biological matrices. However, SERS analysis has been confined to the laboratory due to several reasons such as reproducibility and scalability, both of which have been discussed at length in the literature. Another possible issue with the lack of widespread adoption of SERS is that its application in point of use (POU) testing is only now being fully explored due to the advent of portable Raman spectrometers. Researchers are now investigating how SERS can be used as the output on several POU platforms such as lateral flow assays, wearable sensors, and in volatile organic compound (VOC) detection for human healthcare monitoring, with favorable results that rival the gold standard approaches. Another obstacle that SERS faces is the interpretation of the wealth of information obtained from the platform. To combat this, machine learning is being explored and has been shown to provide quick and accurate analysis of the generated data, leading to sensitive detection and discrimination of many clinically relevant biomolecules. This review will discuss the advancements of SERS combined with POU testing and the strength that machine learning can bring to the analysis to produce a powerful combined platform for human healthcare monitoring.