NASA Ames Research Center, Moffett Field, California 94035, United States.
Variable, Inc., Chattanooga, Tennessee 37406, United States.
ACS Sens. 2023 Jun 23;8(6):2309-2318. doi: 10.1021/acssensors.3c00367. Epub 2023 May 24.
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.
我们改进了现有的、经过太空飞行验证的强大的“电子鼻”(E-Nose),它使用一系列基于电阻变化的纳米传感器来模拟哺乳动物嗅觉的某些方面,通过测量呼出的人类呼吸中挥发性有机化合物(VOCs)的传感器响应模式,来进行现场快速的 COVID-19 感染筛查。我们构建并测试了多个手持式原型 E-Nose 传感器系统,该系统由 64 个化学敏感的纳米材料传感元件组成,这些元件针对 COVID-19 VOC 检测进行了定制;数据采集电子设备;带有用于传感器控制、数据采集和显示的软件(App)的智能平板电脑;以及用于捕获呼出的呼吸样本并将其输送到 E-Nose 内部传感器阵列的采样夹具。传感元件以十亿分之几(ppb)的水平检测到呼吸中典型的 VOC 组合,重复性为 0.02%,再现性为 1.2%;E-Nose 中的测量电子设备提供了与台式仪器相当的测量精度和信噪比。斯坦福大学医学中心对 63 名参与者进行了初步临床测试,他们的 COVID-19 阳性或 COVID-19 阴性状态通过同时进行的 RT-PCR 确定,使用“留一法”训练和分析方法,该方法将这两种人类呼吸进行了 79%的正确识别率的区分。使用先进的机器学习方法,结合体温和其他非侵入性症状筛查分析 E-Nose 响应,利用更广泛人群的更大响应数据库,有望提供更准确的现场答案。进一步的临床测试、设计改进和大规模制造方法是将这项技术部署到诊所、医院、公共场所或家庭中,以快速筛查活跃感染的主要步骤。