Predicting SARS-CoV-2 Variant Using Non-Invasive Hand Odor Analysis: A Pilot Study

Gokool, Vidia A.; Crespo-Cajigas, Janet; Ramírez Torres, Andrea; Forsythe, Liam; Abella, Benjamin S.; Holness, Howard K.; Johnson, Alan Charlie; Postrel, Richard; Furton, Kenneth G.

doi:10.3390/analytica4020016

DOE OSTI · 2005065

Predicting SARS-CoV-2 Variant Using Non-Invasive Hand Odor Analysis: A Pilot Study

Gokool, Vidia A.·Crespo-Cajigas, Janet·Ramírez Torres, Andrea·Forsythe, Liam·Abella, Benjamin S.·Holness, Howard K.·Johnson, Alan Charlie·Postrel, Richard·Furton, Kenneth G.

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Abstract

The adaptable nature of the SARS-CoV-2 virus has led to the emergence of multiple viral variants of concern. This research builds upon a previous demonstration of sampling human hand odor to distinguish SARS-CoV-2 infection status in order to incorporate considerations of the disease variants. This study demonstrates the ability of human odor expression to be implemented as a non-invasive medium for the differentiation of SARS-CoV-2 variants. Volatile organic compounds (VOCs) were extracted from SARS-CoV-2-positive samples using solid phase microextraction (SPME) coupled with gas chromatography–mass spectrometry (GC–MS). Sparse partial least squares discriminant analysis (sPLS-DA) modeling revealed that supervised machine learning could be used to predict the variant identity of a sample using VOC expression alone. The class discrimination of Delta and Omicron BA.5 variant samples was performed with 95.2% (±0.4) accuracy. Omicron BA.2 and Omicron BA.5 variants were correctly classified with 78.5% (±0.8) accuracy. Lastly, Delta and Omicron BA.2 samples were assigned with 71.2% (±1.0) accuracy. This work builds upon the framework of non-invasive techniques producing diagnostics through the analysis of human odor expression, all in support of public health monitoring.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY COVID-19 variants SARS-CoV-2 human scent non-invasive analysis volatile organic compounds

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Gokool, Vidia A., Crespo-Cajigas, Janet, Ramírez Torres, Andrea, Forsythe, Liam, Abella, Benjamin S., Holness, Howard K., Johnson, Alan Charlie, Postrel, Richard, Furton, Kenneth G.. 2023-05-22. Predicting SARS-CoV-2 Variant Using Non-Invasive Hand Odor Analysis: A Pilot Study. https://doi.org/10.3390/analytica4020016

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