Journal Article Journal Article Epublish 12 citations
PubMed: 36421122 DOI: 10.3390/bios12111003
The Use of Biological Sensors and Instrumental Analysis to Discriminate COVID-19 Odor Signatures.
Biosensors (Basel) 12(11)
Authors: Vidia A Gokool, Janet Crespo-Cajigas, Amritha Mallikarjun, Amanda Collins, Sarah A Kane, Victoria Plymouth, Elizabeth Nguyen, Benjamin S Abella, Howard K Holness, Kenneth G Furton, Alan T Charlie Johnson, Cynthia M Otto
Affiliations
- Global Forensic and Justice Center, Department of Chemistry and Biochemistry, Florida International University, Miami, FL 33199, USA.
- Penn Vet Working Dog Center, Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, 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.
Abstract
The spread of SARS-CoV-2, which causes the disease COVID-19, is difficult to control as some positive individuals, capable of transmitting the disease, can be asymptomatic. Thus, it remains critical to generate noninvasive, inexpensive COVID-19 screening systems. Two such methods include detection canines and analytical instrumentation, both of which detect volatile organic compounds associated with SARS-CoV-2. In this study, the performance of trained detection dogs is compared to a noninvasive headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) approach to identifying COVID-19 positive individuals. Five dogs were trained to detect the odor signature associated with COVID-19. They varied in performance, with the two highest-performing dogs averaging 88% sensitivity and 95% specificity over five double-blind tests. The three lowest-performing dogs averaged 46% sensitivity and 87% specificity. The optimized linear discriminant analysis (LDA) model, developed using HS-SPME-GC-MS, displayed a 100% true positive rate and a 100% true negative rate using leave-one-out cross-validation. However, the non-optimized LDA model displayed difficulty in categorizing animal hair-contaminated samples, while animal hair did not impact the dogs' performance. In conclusion, the HS-SPME-GC-MS approach for noninvasive COVID-19 detection more accurately discriminated between COVID-19 positive and COVID-19 negative samples; however, dogs performed better than the computational model when non-ideal samples were presented.
MeSH
Covid-19
Cited by
- Calibrating canines-a universal detector calibrant for detection dogs. Front Allergy, 2024
- Canine detection of chronic wasting disease (CWD) in laboratory and field settings. Prion, 2023
- Expert considerations and consensus for using dogs to detect human SARS-CoV-2-infections. Front Med (Lausanne), 2022
- Prospective fecal microbiomic biomarkers for chronic wasting disease. Microbiol Spectr, 2024
- Multivariate regression modelling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS. PLoS One, 2023
- Investigating the Use of SARS-CoV-2 (COVID-19) Odor Expression as a Non-Invasive Diagnostic Tool-Pilot Study. Diagnostics (Basel), 2023