The Use of Biological Sensors and Instrumental Analysis to Discriminate COVID-19 Odor Signatures
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
Vidia A. Gokool
Global Forensic and Justice Center, Department of Chemistry and Biochemistry, Florida International University, Miami, FL 33199, USA
Janet Crespo-Cajigas
Global Forensic and Justice Center, Department of Chemistry and Biochemistry, Florida International University, Miami, FL 33199, USA
Amritha Mallikarjun
Penn Vet Working Dog Center, Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Amanda Collins
Penn Vet Working Dog Center, Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Sarah A. Kane
Penn Vet Working Dog Center, Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Victoria Plymouth
Penn Vet Working Dog Center, Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Elizabeth Nguyen
Penn Vet Working Dog Center, Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Benjamin S. Abella
Department of Emergency Medicine and Penn Acute Research Collaboration, University of Pennsylvania, Philadelphia, PA 19104, USA
Howard K. Holness
Global Forensic and Justice Center, Department of Chemistry and Biochemistry, Florida International University, Miami, FL 33199, USA
Kenneth G. Furton
Global Forensic and Justice Center, Department of Chemistry and Biochemistry, Florida International University, Miami, FL 33199, USA
Alan T. Charlie Johnson
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
Cynthia M. Otto
Penn Vet Working Dog Center, Clinical Sciences and Advanced Medicine, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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.
