Innovative and versatile surface-enhanced Raman spectroscopy-inspired approaches for viral detection leading to...

The evolution of analytical techniques has opened the possibilities of accurate analyte detection through a straightforward method and short acquisition time, leading towards their applicability to identify medical conditions. Surface-enhanced Raman spectroscopy (SERS) has long been proven effective for rapid detection and relies on SERS spectra that are unique to each specific analyte. However, the complexity of viruses poses challenges to SERS and hinders further progress in its practical applications. The principle of SERS revolves around the interaction among substrate, analyte, and Raman laser, but most studies only emphasize the substrate, especially label-free methods, and the synergy among these factors is often ignored. Therefore, issues related to reproducibility and consistency of results, which are crucial for medical diagnosis and are the main highlights of this review, can be understood and largely addressed when considering these interactions. Viruses are composed of multiple surface components and can be detected by label-free SERS, but the presence of non-target molecules in clinical samples interferes with the detection process. Appropriate spectral data processing workflow also plays an important role in the interpretation of results. Furthermore, integrating machine learning into data processing can account for changes brought about by the presence of non-target molecules when analyzing spectral features to accurately group the data, for example, whether the sample corresponds to a positive or negative patient, and whether a virus variant or multiple viruses are present in the sample. Subsequently, advances in interdisciplinary fields can bring SERS closer to practical applications.