Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning

Ember, K. J. I.; Daoust, F.; Mahfoud, M.; Dallaire, F.; Zamani, E.; Tran, T.; Plante, A.; Diop, M.-K.; Nguyen, T.; St-Georges-Robillard, A.; Ksantini, N.; Lanthier, J.; Filiatrault, A.; Sheehy, G.; Quach, C.; Trudel, D.; Leblond, F.

Significance: The primary method of COVID-19 detection is reverse transcription polymerase chain reaction (RT-PCR) testing. PCR test sensitivity may decrease as more variants of concern arise. Aim: We aimed to develop a reagent-free way to detect COVID-19 in a real-world setting with minimal constraints on sample acquisition. Approach: We present a workflow for collecting, preparing and imaging dried saliva supernatant droplets using a non-invasive, label-free technique known as Raman spectroscopy to detect changes in the molecular profile of saliva associated with COVID-19 infection. Results: Using machine learning and droplet segmentation, amongst all confounding factors, we discriminated between COVID-positive and negative individuals yielding receiver operating coefficient (ROC) curves with an area under curve (AUC) of 0.8 in both males (79% sensitivity, 75% specificity) and females (84% sensitivity, 64% specificity). Taking the sex of the saliva donor into account increased the AUC by 5%. Conclusion:These findings may pave the way for new rapid Raman spectroscopic screening tools for COVID-19 and other infectious diseases.