| Title: | Investigating the Use of SARS-CoV-2 (COVID-19) Odor Expression as a Non-Invasive Diagnostic Tool-Pilot Study |
| Author(s): | Crespo-Cajigas J; Gokool VA; Ramirez Torres A; Forsythe L; Abella BS; Holness HK; Johnson ATC; Postrel R; Furton KG; |
| Address: | "Global Forensic and Justice Center, Department of Chemistry and Biochemistry, Florida International University, Miami, FL 33199, USA. Department of Emergency Medicine and Penn Acute Research Collaboration, University of Pennsylvania, Philadelphia, PA 19104, USA. Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA. VOC Health, Inc., Miami Beach, FL 33140, USA" |
| DOI: | 10.3390/diagnostics13040707 |
| ISSN/ISBN: | 2075-4418 (Print) 2075-4418 (Electronic) 2075-4418 (Linking) |
| Abstract: | "Since the beginning of the COVID-19 pandemic, there has been enormous interest in the development of measures that would allow for the swift detection of the disease. The rapid screening and preliminary diagnosis of SARS-CoV-2 infection allow for the instant identification of possibly infected individuals and the subsequent mitigation of the disease spread. Herein, the detection of SARS-CoV-2-infected individuals was explored using noninvasive sampling and low-preparatory-work analytical instrumentation. Hand odor samples were obtained from SARS-CoV-2-positive and -negative individuals. The volatile organic compounds (VOCs) were extracted from the collected hand odor samples using solid phase microextraction (SPME) and analyzed using gas chromatography coupled with mass spectrometry (GC-MS). Sparse partial least squares discriminant analysis (sPLS-DA) was used to develop predictive models using the suspected variant sample subsets. The developed sPLS-DA models performed moderately (75.8% (+/-0.4) accuracy, 81.8% sensitivity, 69.7% specificity) at distinguishing between SARS-CoV-2-positive and negative -individuals based on the VOC signatures alone. Potential markers for distinguishing between infection statuses were preliminarily acquired using this multivariate data analysis. This work highlights the potential of using odor signatures as a diagnostic tool and sets the groundwork for the optimization of other rapid screening sensors such as e-noses or detection canines" |
| Keywords: | Covid-19 Hs-spme-gc-ms SARS-CoV-2 machine learning non-invasive diagnostic tool odor signature sPLS-DA modeling; |
| Notes: | "PubMed-not-MEDLINECrespo-Cajigas, Janet Gokool, Vidia A Ramirez Torres, Andrea Forsythe, Liam Abella, Benjamin S Holness, Howard K Johnson, Alan T Charlie Postrel, Richard Furton, Kenneth G eng U18 TR003775/TR/NCATS NIH HHS/ 1-U18-Tr-003775-01/TR/NCATS NIH HHS/ Switzerland 2023/02/26 Diagnostics (Basel). 2023 Feb 13; 13(4):707. doi: 10.3390/diagnostics13040707" |
