Proof of concept for real-time detection of SARS CoV-2 infection with an electronic nose

Abstract

Rapid diagnosis is key to curtailing the Covid-19 pandemic. One path to such rapid diagnosis may rely on identifying volatile organic compounds (VOCs) emitted by the infected body, or in other words, identifying the smell of the infection. Consistent with this rationale, dogs can use their nose to identify Covid-19 patients. Given the scale of the pandemic, however, animal deployment is a challenging solution. In contrast, electronic noses (eNoses) are machines aimed at mimicking animal olfaction, and these can be deployed at scale. To test the hypothesis that SARS CoV-2 infection is associated with a body-odor detectable by an eNose, we placed a generic eNose in-line at a drive-through testing station. We applied a deep learning classifier to the eNose measurements, and achieved real-time detection of SARS CoV-2 infection at a level significantly better than chance, for both symptomatic and non-symptomatic participants. This proof of concept with a generic eNose implies that an optimized eNose may allow effective real-time diagnosis, which would provide for extensive relief in the Covid-19 pandemic.

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose

“…After demonstrating the hybrid learning method to reduce the required number of sensors without downgrading the system quality, we evaluated the GeNose C19 in terms of its performances in comparison to other commercial and developed e-nose devices (e.g., PEN3 e-nose, aeoNose, SpiroNose, and nanomaterial-based e-nose [42] , [43] , [44] , [45] ), which have been routinely tested for COVID-19 detection ( Table 4 ). Here, several key parameters are compared (i.e., sensor type, sensor number, breath sample number, positive rate of samples, measurement time, and results during assessment in exhaled breath).…”

“… Electronic nose (e-nose) technology Number of sensors Number of samples Positive rate of samples Measurement time Results Ref. MOS sensor (PEN3 e-nose) 10 503 5.4% 80 s 66.7% of true positive rate [42] MOS sensor (aeoNose) 3 219 26.0% 300 s 86% of sensitivity and 92% of negative predictive value [43] MOS sensor (SpiroNose) 7 4510 7.7% Not available 93.1% of ROC-AUC [44] Multiplexed nanomaterial-based chemoresistive sensor 8 130 37.7% 3 s 100% of sensitivity and 61% of specificity [45] MOS sensor (GeNose C19) 10 reduced to 5 460 50.0% 45 s (88 ± 6)% of cross-validation sensitivity and (84 ± 6)% of cross-validation specificity This work …”

“…Recently, several proof-of-concept studies for the real-time detection of the SARS-CoV-2 infection with the e-nose have been reported in several countries. First, in Tel Aviv, Israel, a compact PEN3 e-nose (AIRSENSE Analytics GmbH, Schwerin, Germany) had been successfully tested at a drive-through testing station using a one-way disposable sampling valve method to protect tested participants [42] . Second, in the Netherlands, another commercial e-nose so-called aeoNose (The eNose Company, Zutphen, the Netherlands) integrating three types of micro-hotplate MOS sensors (i.e., VOC, carbon monoxide, and nitrogen dioxide sensors) had been used for pre-operative SARS-CoV-2 screening at the Maastricht University Medical Center (MUMC+) [43] .…”

Rational Design of Peptides Derived from Odorant-Binding Proteins for SARS-CoV-2-Related Volatile Organic Compounds Recognition

“…Based on recent literature, five representative SARS-CoV-2-related VOC biomarkers, ethanol, nonanal, benzaldehyde, acetic acid, and acetone from exhaled breath samples, were selected [17,[29][30][31][32]. The structures of these VOC biomarkers are shown in Figure 1.…”

An Experimental Apparatus for E-Nose Breath Analysis in Respiratory Failure Patients

“…In the present study, we aimed to develop a novel experimental set-up suitable for sampling exhaled breath at the bedside in patients with acute respiratory failure. Further-more, we ran a feasibility study using this novel system in (1) patients with respiratory failure due to SARS-CoV-2, (2) patients with SARS-CoV-2 infection without respiratory failure, and (3) controls [33][34][35][36]. In a preliminary analysis, we evaluated whether our system could differentiate respiratory failure patients from controls.…”