| Title: | "Electronic Nose Development and Preliminary Human Breath Testing for Rapid, Non-Invasive COVID-19 Detection" |
| Author(s): | Li J; Hannon A; Yu G; Idziak LA; Sahasrabhojanee A; Govindarajan P; Maldonado YA; Ngo K; Abdou JP; Mai N; Ricco AJ; |
| Address: | "NASA Ames Research Center, Moffett Field, California 94035, United States. Variable, Inc., Chattanooga, Tennessee 37406, United States. School of Medicine, Stanford University, Stanford, California 94305, United States" |
| DOI: | 10.1021/acssensors.3c00367 |
| ISSN/ISBN: | 2379-3694 (Electronic) 2379-3694 (Linking) |
| Abstract: | "We adapted an existing, spaceflight-proven, robust 'electronic nose' (E-Nose) that uses an array of electrical resistivity-based nanosensors mimicking aspects of mammalian olfaction to conduct on-site, rapid screening for COVID-19 infection by measuring the pattern of sensor responses to volatile organic compounds (VOCs) in exhaled human breath. We built and tested multiple copies of a hand-held prototype E-Nose sensor system, composed of 64 chemically sensitive nanomaterial sensing elements tailored to COVID-19 VOC detection; data acquisition electronics; a smart tablet with software (App) for sensor control, data acquisition and display; and a sampling fixture to capture exhaled breath samples and deliver them to the sensor array inside the E-Nose. The sensing elements detect the combination of VOCs typical in breath at parts-per-billion (ppb) levels, with repeatability of 0.02% and reproducibility of 1.2%; the measurement electronics in the E-Nose provide measurement accuracy and signal-to-noise ratios comparable to benchtop instrumentation. Preliminary clinical testing at Stanford Medicine with 63 participants, their COVID-19-positive or COVID-19-negative status determined by concomitant RT-PCR, discriminated between these two categories of human breath with a 79% correct identification rate using 'leave-one-out' training-and-analysis methods. Analyzing the E-Nose response in conjunction with body temperature and other non-invasive symptom screening using advanced machine learning methods, with a much larger database of responses from a wider swath of the population, is expected to provide more accurate on-the-spot answers. Additional clinical testing, design refinement, and a mass manufacturing approach are the main steps toward deploying this technology to rapidly screen for active infection in clinics and hospitals, public and commercial venues, or at home" |
| Keywords: | Animals Humans Electronic Nose Reproducibility of Results *COVID-19/diagnosis *Nanostructures Breath Tests/methods *Volatile Organic Compounds/analysis Mammals Covid-19 infection detection nanomaterial sensor array; |
| Notes: | "MedlineLi, Jing Hannon, Ami Yu, George Idziak, Luke A Sahasrabhojanee, Adwait Govindarajan, Prasanthi Maldonado, Yvonne A Ngo, Khoa Abdou, John P Mai, Nghia Ricco, Antonio J eng 2023/05/24 ACS Sens. 2023 Jun 23; 8(6):2309-2318. doi: 10.1021/acssensors.3c00367. Epub 2023 May 24" |
