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

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.

Discrimination of SARS-CoV-2 infected patient samples by detection dogs: A proof of concept study.

While the world awaits a widely available COVID-19 vaccine, availability of testing is limited in many regions and can be further compounded by shortages of reagents, prolonged processing time and delayed results. One approach to rapid testing is to leverage the volatile organic compound (VOC) signature of SARS-CoV-2 infection. Detection dogs, a biological sensor of VOCs, were utilized to investigate whether SARS-CoV-2 positive urine and saliva patient samples had a unique odor signature. The virus was inactivated in all training samples with either detergent or heat treatment. Using detergent-inactivated urine samples, dogs were initially trained to find samples collected from hospitalized patients confirmed with SARS-CoV-2 infection, while ignoring samples collected from controls. Dogs were then tested on their ability to spontaneously recognize heat-treated urine samples as well as heat-treated saliva from hospitalized SARS-CoV-2 positive patients. Dogs successfully discriminated between infected and uninfected urine samples, regardless of the inactivation protocol, as well as heat-treated saliva samples. Generalization to novel samples was limited, particularly after intensive training with a restricted sample set. A unique odor associated with SARS-CoV-2 infection present in human urine as well as saliva, provides impetus for the development of odor-based screening, either by electronic, chemical, or biological sensing methods. The use of dogs for screening in an operational setting will require training with a large number of novel SARS-CoV-2 positive and confirmed negative samples.