{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T13:31:42Z","timestamp":1784295102866,"version":"3.55.0"},"reference-count":113,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002631","name":"Gachon University research fund of 2025","doi-asserted-by":"publisher","award":["GCU-202502820001"],"award-info":[{"award-number":["GCU-202502820001"]}],"id":[{"id":"10.13039\/501100002631","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Gas sensors provide versatile opportunities for detecting volatile organic compounds (VOCs) such as acetone, methanol, ethanol, propanol, isoprene, and aldehydes in exhaled breath (EB) associated with COVID-19 respiratory infections. These VOCs provide valuable information about metabolic markers linked with COVID-19. They have opened opportunities to develop sensors for COVID-19 screening based on breath analysis. These sensors have the potential to provide the rapid detection of viruses in healthcare settings. RT-PCR, as a conventionally adopted diagnostic method, has a detection limit around 10\u2013100 RNA copies\/mL, with an accuracy of around 95%. Gas sensors have demonstrated VOC detection limits at the ppm level in COVID-19 EB and have displayed a sensitivity and specificity of 98.2% and 74.3%, respectively. Multiple gas sensors combined with machine learning algorithms have the potential to enhance the specificity of VOC detection. In addition to having an accuracy similar to that of the PCR method, the VOC-based diagnosis of COVID-19 offers unique advantages in terms of non-invasive and rapid detection. This review provides an overview of state-of-the-art gas sensors developed for COVID-19 detection. Despite there being significant developments in this field, there are certain challenges that still need to be addressed\u2014these include the impact of environmental factors, the specificity of detection, the sensing range, and precision limitations, leading to accuracy issues. Despite these existing challenges, the integration of gas sensors with machine learning methods can enhance the accuracy of the detection of COVID-19. Future research directions are proposed to validate and standardize the application of gas sensors for COVID-19 in clinical settings.<\/jats:p>","DOI":"10.3390\/bios15120792","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:42:02Z","timestamp":1764960122000},"page":"792","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Opportunities and Challenges in Gas Sensor Technologies for Accurate Detection of COVID-19"],"prefix":"10.3390","volume":"15","author":[{"given":"Masoom","family":"Fatima","sequence":"first","affiliation":[{"name":"Formerly at Department of Biology, Allama Iqbal Open University, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1212-9667","authenticated-orcid":false,"given":"Munazza","family":"Fatima","sequence":"additional","affiliation":[{"name":"Department of Microbiology, College of Medicine, Gachon University, Incheon 21936, Republic of Korea"},{"name":"Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1340-1615","authenticated-orcid":false,"given":"Naseem","family":"Abbas","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3024-3439","authenticated-orcid":false,"given":"Pil-Gu","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Life Science, Gachon University, Seongnam 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/S1473-3099(20)30243-7","article-title":"Estimates of the severity of coronavirus disease 2019: A model-based analysis","volume":"20","author":"Verity","year":"2020","journal-title":"Lancet Infect. 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