Detection of several respiratory viruses with Surface-Enhanced Raman Spectroscopy coupled with Artificial Intelligence

Diagnoses of viral infections are a challenge when facing a crisis like COVID-19, where their speed and reliability are critical to minimize diseases spread. The gold standard of diagnostics, quantitative Polymerase Chain Reaction, is time- and reagent-consuming and requires qualified personnel. Therefore, it is necessary to find new detection techniques to overcome these barriers. Surface Enhanced Raman Spectroscopy (SERS) is a detection method, based on light and metallic particles admixed with the samples, already used in different fields of research. In this study, we discriminate three respiratory viruses using a combination of SERS and Artificial Intelligence (AI). Our technique appears to be fast, reproducible, and reliable, achieving between 95% and 100% of accuracy, standing out as a powerful tool usable for viral diagnostics.