The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach has been widely used to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, instead of using it alone, clinicians often prefer to diagnose the coronavirus disease 2019 (COVID-19) by utilizing a combination of clinical signs and symptoms, laboratory test, imaging measurement (e.g., chest computed tomography scan), and multivariable clinical prediction models, including the electronic nose. Here, we report on the development and use of a low cost, noninvasive method to rapidly sniff out COVID-19 based on a portable electronic nose (GeNose C19) integrating an array of metal oxide semiconductor gas sensors, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total of 615 breath samples composed of 333 positive and 282 negative samples. The samples were obtained from 43 positive and 40 negative COVID-19 patients, respectively, and confirmed with RT-qPCR at two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis, support vector machine, stacked multilayer perceptron, and deep neural network) were utilized to identify the top-performing pattern recognition methods and to obtain a high system detection accuracy (88–95%), sensitivity (86–94%), and specificity (88–95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.

逆转录定量聚合酶链反应 (RT-qPCR) 方法已广泛用于检测严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2)。然而，临床医生通常更喜欢结合临床体征和症状、实验室检查、影像学测量（例如胸部计算机断层扫描）和多变量临床预测来诊断 2019 年冠状病毒病 (COVID-19)，而不是单独使用它型号，包括电子鼻。在这里，我们报告了一种低成本、非侵入性方法的开发和使用，该方法基于便携式电子鼻 (GeNose C19) 快速嗅出 COVID-19，该电子鼻集成了一系列金属氧化物半导体气体传感器、优化的特征提取和机器学习楷模。这种方法在分析测试中进行了评估，共涉及 615 个呼吸样本，其中包括 333 个阳性样本和 282 个阴性样本。这些样本分别取自 43 名 COVID-19 阳性患者和 40 名阴性 COVID-19 患者，并在位于印度尼西亚日惹特区的两家医院通过 RT-qPCR 进行了确认。四种不同的机器学习算法（即线性判别分析、支持向量机、堆叠多层感知器和深度神经网络）被用于识别最佳模式识别方法并获得高系统检测精度（88-95%） 、灵敏度 (86–94%) 和来自测试数据集的特异性 (88–95%) 水平。我们的结果表明，GeNose C19 可以被认为是一种非常有潜力的呼气测醉器，可用于快速筛查 COVID-19。