COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections

The pandemic of COVID-19 is continuously spreading, becoming a worldwide emergency. Early and fast identification of subjects with a current or past infection must be achieved to slow down the epidemiological widening. Here we report a Raman-based approach for the analysis of saliva, able to significantly discriminate the signal of patients with a current infection by COVID-19 from healthy subjects and/or subjects with a past infection. Our results demonstrated the differences in saliva biochemical composition of the three experimental groups, with modifications grouped in specific attributable spectral regions. The Raman-based classification model was able to discriminate the signal collected from COVID-19 patients with accuracy, precision, sensitivity and specificity of more than 95%. In order to translate this discrimination from the signal-level to the patient-level, we developed a Deep Learning model obtaining accuracy in the range 89-92%. These findings have implications for the creation of a potential Raman-based diagnostic tool, using saliva as minimal invasive and highly informative biofluid, demonstrating the efficacy of the classification model.

Automated summary

The study presents a Raman-based approach for analyzing saliva to differentiate signals from COVID-19 patients and others, demonstrating high accuracy, precision, sensitivity, and specificity in classifying patient signals. By developing a deep learning model, the discrimination of patient signals is translated to the patient level with an accuracy range of 89-92%. These findings suggest the potential of a Raman-based diagnostic tool using saliva as a minimally invasive and highly informative biofluid.