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Biosensors (Basel)

Title:		The Use of Biological Sensors and Instrumental Analysis to Discriminate COVID-19 Odor Signatures
Author(s):		Gokool VA; Crespo-Cajigas J; Mallikarjun A; Collins A; Kane SA; Plymouth V; Nguyen E; Abella BS; Holness HK; Furton KG; Johnson ATC; Otto CM;
Address:		"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"
Journal Title:		Biosensors (Basel)
Year:		2022
Volume:		20221111
Issue:		11
Page Number:		-
DOI:		10.3390/bios12111003
ISSN/ISBN:		2079-6374 (Electronic) 2079-6374 (Linking)
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"
Keywords:		Dogs Animals *Odorants/analysis *COVID-19/diagnosis SARS-CoV-2 Solid Phase Microextraction/methods Gas Chromatography-Mass Spectrometry/methods Covid-19 Spme-gc-ms VOCs canine detection odor signatures;
Notes:		"MedlineGokool, Vidia A Crespo-Cajigas, Janet Mallikarjun, Amritha Collins, Amanda Kane, Sarah A Plymouth, Victoria Nguyen, Elizabeth Abella, Benjamin S Holness, Howard K Furton, Kenneth G Johnson, Alan T Charlie Otto, Cynthia M eng P30 ES013508/ES/NIEHS NIH HHS/ 1-U18-Tr-003775-01./NH/NIH HHS/ Randomized Controlled Trial Switzerland 2022/11/25 Biosensors (Basel). 2022 Nov 11; 12(11):1003. doi: 10.3390/bios12111003"