{"doi":"10.3390/bios12111003","title":"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.","journal":"Biosensors","year":2022,"id":271262,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":13,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9605,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":936135,"name":"Janet Crespo‐Cajigas","orcid":"0000-0002-8208-1216","position":1,"is_corresponding":false},{"id":936136,"name":"A. Mallikarjun","orcid":"0000-0002-9100-5004","position":2,"is_corresponding":false},{"id":936651,"name":"Amanda Collins","orcid":null,"position":3,"is_corresponding":false},{"id":657239,"name":"Sarah A. Kane","orcid":"0000-0001-5417-9913","position":4,"is_corresponding":false},{"id":657896,"name":"Victoria L. Plymouth","orcid":null,"position":5,"is_corresponding":false},{"id":936137,"name":"Elizabeth Nguyen","orcid":"0000-0003-3424-9771","position":6,"is_corresponding":false},{"id":3835,"name":"Benjamin S. Abella","orcid":"0000-0003-2521-0891","position":7,"is_corresponding":false},{"id":936138,"name":"H. Holness","orcid":"0000-0002-9629-049X","position":8,"is_corresponding":false},{"id":936139,"name":"Kenneth G. Furton","orcid":"0000-0003-2941-2597","position":9,"is_corresponding":false},{"id":522056,"name":"A. T. Charlie Johnson","orcid":"0000-0002-5402-1224","position":10,"is_corresponding":false},{"id":497593,"name":"Cynthia M. Otto","orcid":"0000-0003-0846-2114","position":11,"is_corresponding":false},{"id":936134,"name":"Vidia A. Gokool","orcid":"0000-0003-1867-1761","position":0,"is_corresponding":true}],"reference_count":64,"raw_metadata":null,"created_at":"2026-07-19T00:27:35.206187Z","pmid":"36421122","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}