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Proof of concept for real-time detection of SARS CoV-2 infection with an electronic nose

Author

Listed:
  • Kobi Snitz
  • Michal Andelman-Gur
  • Liron Pinchover
  • Reut Weissgross
  • Aharon Weissbrod
  • Eva Mishor
  • Roni Zoller
  • Vera Linetsky
  • Abebe Medhanie
  • Sagit Shushan
  • Eli Jaffe
  • Noam Sobel

Abstract

Rapid diagnosis is key to curtailing the Covid-19 pandemic. One path to such rapid diagnosis may rely on identifying volatile organic compounds (VOCs) emitted by the infected body, or in other words, identifying the smell of the infection. Consistent with this rationale, dogs can use their nose to identify Covid-19 patients. Given the scale of the pandemic, however, animal deployment is a challenging solution. In contrast, electronic noses (eNoses) are machines aimed at mimicking animal olfaction, and these can be deployed at scale. To test the hypothesis that SARS CoV-2 infection is associated with a body-odor detectable by an eNose, we placed a generic eNose in-line at a drive-through testing station. We applied a deep learning classifier to the eNose measurements, and achieved real-time detection of SARS CoV-2 infection at a level significantly better than chance, for both symptomatic and non-symptomatic participants. This proof of concept with a generic eNose implies that an optimized eNose may allow effective real-time diagnosis, which would provide for extensive relief in the Covid-19 pandemic.

Suggested Citation

  • Kobi Snitz & Michal Andelman-Gur & Liron Pinchover & Reut Weissgross & Aharon Weissbrod & Eva Mishor & Roni Zoller & Vera Linetsky & Abebe Medhanie & Sagit Shushan & Eli Jaffe & Noam Sobel, 2021. "Proof of concept for real-time detection of SARS CoV-2 infection with an electronic nose," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-12, June.
  • Handle: RePEc:plo:pone00:0252121
    DOI: 10.1371/journal.pone.0252121
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    References listed on IDEAS

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    1. Hong-Min Jeon & Je-Yeol Lee & Gu-Min Jeong & Sang-Il Choi, 2018. "Data reconstruction using iteratively reweighted L1-principal component analysis for an electronic nose system," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-19, July.
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