The Use of Biological Sensors and Instrumental Analysis to Discriminate COVID-19 Odor Signatures

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.

Canine detection of chronic wasting disease (CWD) in laboratory and field settings

“…It is unknown, however, whether the prion seeding process affects the characteristic VOCs of CWD; the dogs may utilize a set of biomarkers unaffected or minimally affected by the freeze–thaw cycles. Dogs do not require the presence of the disease-causing agent to detect disease presence in a sample; for example, dogs can detect sweat from patients with COVID-19 [ 46 , 47 ], even though sweat does not carry the virus itself [ 48 ]. Further research is necessary to determine how prion seeding activity affects the signature odour of CWD detected by the dogs.…”

Multivariate regression modelling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS

“…This pre-processing feature can be completed manually or with the aid of commercially available software products such as Sepsolve’s ChromCompare program. The current dataset was retention time aligned using a bin size of 0.040 minutes, the procedure was conducted using a custom script in R, a similar approach has been used in the pre-processing of other HS-SPME-GC-MS collected human odor datasets [ 23 , 24 ].…”

Expert considerations and consensus for using dogs to detect human SARS-CoV-2-infections

“…This could complicate the creation of a long-lasting training sample set if not stored appropriately, but this remains speculative and needs to be elucidated in future studies. For example, Gokool et al provided preliminary evidence that the specific odor persists for months in worn cotton shirts ( 193 ). Sweat samples for COVID-19-detection were stored cooled or at room temperature for around 2 h ( 194 ), 24–72 h ( 70 , 185 – 187 , 189 , 190 , 192 ), 1 week ( 188 ), or even up to 6 months in triple zip-lock plastic bags ( 69 ) before being presented to a dog.…”