Raman spectroscopy-based detection of RNA viruses in saliva: A preliminary report

J Biophotonics. 2020 Oct;13(10):e202000189. doi: 10.1002/jbio.202000189. Epub 2020 Aug 10.


Several non-invasive Raman spectroscopy-based assays have been reported for rapid and sensitive detection of pathogens. We developed a novel statistical model for the detection of RNA viruses in saliva, based on an unbiased selection of a set of 65 Raman spectral features that mostly attribute to the RNA moieties, with a prediction accuracy of 91.6% (92.5% sensitivity and 88.8% specificity). Furthermore, to minimize variability and automate the downstream analysis of the Raman spectra, we developed a GUI-based analytical tool "RNA Virus Detector (RVD)." This conceptual framework to detect RNA viruses in saliva could form the basis for field application of Raman Spectroscopy in managing viral outbreaks, such as the ongoing COVID-19 pandemic. (http://www.actrec.gov.in/pi-webpages/AmitDutt/RVD/RVD.html).

Keywords: GUI-based automated computational analysis; RNA virus; Raman spectroscopy; linear discriminant analysis; principal component analysis.

MeSH terms

  • HEK293 Cells
  • Humans
  • RNA Viruses / isolation & purification*
  • Saliva / virology*
  • Spectrum Analysis, Raman / methods*
  • User-Computer Interface