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

Gokool, VA, Crespo-Cajigas, J, Ramírez Torres, A et al. (2023). Predicting SARS-CoV-2 Variant Using Non-Invasive Hand Odor Analysis: A Pilot Study . 4(2), 206-216. 10.3390/analytica4020016

cited authors

  • Gokool, VA; Crespo-Cajigas, J; Ramírez Torres, A; Forsythe, L; Abella, BS; Holness, HK; Johnson, ATC; Postrel, R; Furton, KG


  • 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.

publication date

  • June 1, 2023

Digital Object Identifier (DOI)

start page

  • 206

end page

  • 216


  • 4


  • 2