Electronic Nose Development and Preliminary Human Breath Testing for Rapid, Non-Invasive COVID-19 Detection.

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.

Breath Analysis of COVID-19 Patients in a Tertiary UK Hospital by Optical Spectrometry: The E-Nose CoVal Study.

Throughout the SARS-CoV-2 pandemic, diagnostic technology played a crucial role in managing outbreaks on a national and global level. One diagnostic modality that has shown promise is breath analysis, due to its non-invasive nature and ability to give a rapid result. In this study, a portable FTIR (Fourier Transform Infra-Red) spectrometer was used to detect chemical components in the breath from Covid positive symptomatic and asymptomatic patients versus a control cohort of Covid negative patients. Eighty-five patients who had a nasopharyngeal polymerase chain reaction (PCR) test for the detection of SARS-CoV-2 within the last 5 days were recruited to the study (36 symptomatic PCR positive, 23 asymptomatic PCR positive and 26 asymptomatic PCR negative). Data analysis indicated significant difference between the groups, with SARS-CoV-2 present on PCR versus the negative PCR control group producing an area under the curve (AUC) of 0.87. Similar results were obtained comparing symptomatic versus control and asymptomatic versus control. The asymptomatic results were higher than the symptomatic (0.88 vs. 0.80 AUC). When analysing individual chemicals, we found ethanol, methanol and acetaldehyde were the most important, with higher concentrations in the COVID-19 group, with symptomatic patients being higher than asymptomatic patients. This study has shown that breath analysis can provide significant results that distinguish patients with or without COVID-19 disease/carriage.

Diagnostic performance of eNose technology in COVID-19 patients after hospitalization.

BACKGROUND: Volatile organic compounds (VOCs) produced by human cells reflect metabolic and pathophysiological processes which can be detected with the use of electronic nose (eNose) technology. Analysis of exhaled breath may potentially play an important role in diagnosing COVID-19 and stratification of patients based on pulmonary function or chest CT. METHODS: Breath profiles of COVID-19 patients were collected with an eNose device (SpiroNose) 3 months after discharge from the Leiden University Medical Centre and matched with breath profiles from healthy individuals for analysis. Principal component analysis was performed with leave-one-out cross validation and visualised with receiver operating characteristics. COVID-19 patients were stratified in subgroups with a normal pulmonary diffusion capacity versus patients with an impaired pulmonary diffusion capacity (DLCOc < 80% of predicted) and in subgroups with a normal chest CT versus patients with COVID-19 related chest CT abnormalities. RESULTS: The breath profiles of 135 COVID-19 patients were analysed and matched with 174 healthy controls. The SpiroNose differentiated between COVID-19 after hospitalization and healthy controls with an AUC of 0.893 (95-CI, 0.851-0.934). There was no difference in VOCs patterns in subgroups of COVID-19 patients based on diffusion capacity or chest CT. CONCLUSIONS: COVID-19 patients have a breath profile distinguishable from healthy individuals shortly after hospitalization which can be detected using eNose technology. This may suggest ongoing inflammation or a common repair mechanism. The eNose could not differentiate between subgroups of COVID-19 patients based on pulmonary diffusion capacity or chest CT.

Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers.

The established efficacy of electronic volatile organic compound (VOC) detection technologies as diagnostic tools for noninvasive early detection of COVID-19 and related coronaviruses has been demonstrated from multiple studies using a variety of experimental and commercial electronic devices capable of detecting precise mixtures of VOC emissions in human breath. The activities of numerous global research teams, developing novel electronic-nose (e-nose) devices and diagnostic methods, have generated empirical laboratory and clinical trial test results based on the detection of different types of host VOC-biomarker metabolites from specific chemical classes. COVID-19-specific volatile biomarkers are derived from disease-induced changes in host metabolic pathways by SARS-CoV-2 viral pathogenesis. The unique mechanisms proposed from recent researchers to explain how COVID-19 causes damage to multiple organ systems throughout the body are associated with unique symptom combinations, cytokine storms and physiological cascades that disrupt normal biochemical processes through gene dysregulation to generate disease-specific VOC metabolites targeted for e-nose detection. This paper reviewed recent methods and applications of e-nose and related VOC-detection devices for early, noninvasive diagnosis of SARS-CoV-2 infections. In addition, metabolomic (quantitative) COVID-19 disease-specific chemical biomarkers, consisting of host-derived VOCs identified from exhaled breath of patients, were summarized as possible sources of volatile metabolic biomarkers useful for confirming and supporting e-nose diagnoses.

Assessment of an e-nose performance for the detection of COVID-19 specific biomarkers.

Early, rapid and non-invasive diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is needed for the prevention and control of coronavirus disease 2019 (COVID-19). COVID-19 mainly affects the respiratory tract and lungs. Therefore, analysis of exhaled breath could be an alternative scalable method for reliable SARS-CoV-2 screening. In the current study, an experimental protocol using an electronic-nose ('e-nose') for attempting to identify a specific respiratory imprint in COVID-19 patients was optimized. Thus the analytical performances of the Cyranose®, a commercial e-nose device, were characterized under various controlled conditions. In addition, the effect of various experimental conditions on its sensor array response was assessed, including relative humidity, sampling time and flow rate, aiming to select the optimal parameters. A statistical data analysis was applied to e-nose sensor response using common statistical analysis algorithms in an attempt to demonstrate the possibility to detect the presence of low concentrations of spiked acetone and nonanal in the breath samples of a healthy volunteer. Cyranose®reveals a possible detection of low concentrations of these two compounds, in particular of 25 ppm nonanal, a possible marker of SARS-CoV-2 in the breath.

Determination of global chemical patterns in exhaled breath for the discrimination of lung damage in postCOVID patients using olfactory technology.

The objective of this work was to evaluate the use of an electronic nose and chemometric analysis to discriminate global patterns of volatile organic compounds (VOCs) in breath of postCOVID syndrome patients with pulmonary sequelae. A cross-sectional study was performed in two groups, the group 1 were subjects recovered from COVID-19 without lung damage and the group 2 were subjects recovered from COVID-19 with impaired lung function. The VOCs analysis was executed using a Cyranose 320 electronic nose with 32 sensors, applying principal component analysis (PCA), Partial Least Square-Discriminant Analysis, random forest, canonical discriminant analysis (CAP) and the diagnostic power of the test was evaluated using the ROC (Receiver Operating Characteristic) curve. A total of 228 participants were obtained, for the postCOVID group there are 157 and 71 for the control group, the chemometric analysis results indicate in the PCA an 84% explanation of the variability between the groups, the PLS-DA indicates an observable separation between the groups and 10 sensors related to this separation, by random forest, a classification error was obtained for the control group of 0.090 and for the postCOVID group of 0.088 correct classification. The CAP model showed 83.8% of correct classification and the external validation of the model showed 80.1% of correct classification. Sensitivity and specificity reached 88.9% (73.9%-96.9%) and 96.9% (83.7%-99.9%) respectively. It is considered that this technology can be used to establish the starting point in the evaluation of lung damage in postCOVID patients with pulmonary sequelae.

Clinical studies of detecting COVID-19 from exhaled breath with electronic nose.

The COVID-19 pandemic has attracted numerous research studies because of its impact on society and the economy. The pandemic has led to progress in the development of diagnostic methods, utilizing the polymerase chain reaction (PCR) as the gold standard for coronavirus SARS-CoV-2 detection. Numerous tests can be used at home within 15 min or so but of with lower accuracy than PCR. There is still a need for point-of-care tests available for mass daily screening of large crowds in airports, schools, and stadiums. The same problem exists with fast and continuous monitoring of patients during their medical treatment. The rapid methods can use exhaled breath analysis which is non-invasive and delivers the result quite fast. Electronic nose can detect a cocktail of volatile organic com-pounds (VOCs) induced by virus infection and disturbed metabolism in the human body. In our exploratory studies, we present the results of COVID-19 detection in a local hospital by applying the developed electronic setup utilising commercial VOC gas sensors. We consider the technical problems noticed during the reported studies and affecting the detection results. We believe that our studies help to advance the proposed technique to limit the spread of COVID-19 and similar viral infections.

Visual diagnosis of COVID-19 disease based on serum metabolites using a paper-based electronic tongue.

This study aims to use a paper-based sensor array for point-of-care detection of COVID-19 diseases. Various chemical compounds such as nanoparticles, organic dyes and metal ion complexes were employed as sensing elements in the array fabrication, capturing the metabolites of human serum samples. The viral infection caused the type and concentration of serum compositions to change, resulting in different color responses for the infected and control samples. For this purpose, 118 serum samples of COVID-19 patients and non-COVID controls both men and women with the age range of 14-88 years were collected. The serum samples were initially subjected to the sensor, followed by monitoring the variation in the color of sensing elements for 5 min using a scanner. By taking into consideration the statistical information, this method was capable of discriminating COVID-19 patients and control samples with 83.0% accuracy. The variation of age did not influence the colorimetric patterns. The desirable correlation was observed between the sensor responses and viral load values calculated by the PCR test, proposing a rapid and facile way to estimate the disease severity. Compared to other rapid detection methods, the developed assay is cost-effective and user-friendly, allowing for screening COVID-19 diseases reliably.

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

Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance.

A colorimetric electronic tongue for point-of-care detection of COVID-19 using salivary metabolites.

The monitoring of profile concentrations of chemical markers in saliva samples can be used to diagnose COVID-19 patients, and differentiate them from healthy individuals. Here, this purpose is achieved by designing a paper-based colorimetric sensor with an origami structure, containing general receptors such as pH-sensitive organic dyes, Lewis donors or acceptors, functionalized nanoparticles, and ion metal complexes. The color changes taking place in the receptors in the presence of chemical markers are visually observed and recorded with a digital instrument. Different types and amounts of the chemical markers provide the sensor with a unique response for patients (60 samples) or healthy (55 samples) individuals. These two categories can be discriminated with 84.3% accuracy. This study evidences that the saliva composition of cured and healthy participants is different from each other with accuracy of 85.7%. Moreover, viral load values obtained from the rRT-PCR method can be estimated by the designed sensor. Besides COVID-19, it may possible to simultaneously identify smokers and people with kidney disease and diabetes using the specified electronic tongue. Due to its high efficiency, the prepared paper device can be employed as a rapid detection kit to detect COVID-19.

Post-COVID syndrome screening through breath analysis using electronic nose technology.

There is an urgent need to have reliable technologies to diagnose post-coronavirus disease syndrome (PCS), as the number of people affected by COVID-19 and related complications is increasing worldwide. Considering the amount of risks associated with the two chronic lung diseases, asthma and chronic obstructive pulmonary disease (COPD), there is an immediate requirement for a screening method for PCS, which also produce symptoms similar to these conditions, especially since very often, many COVID-19 cases remain undetected because a good share of such patients is asymptomatic. Breath analysis techniques are getting attention since they are highly non-invasive methods for disease diagnosis, can be implemented easily for point-of-care applications even in primary health care centres. Electronic (E-) nose technology is coming up with better reliability, ease of operation, and affordability to all, and it can generate signatures of volatile organic compounds (VOCs) in exhaled breath as markers of diseases. The present report is an outcome of a pilot study using an E-nose device on breath samples of cohorts of PCS, asthma, and normal (control) subjects. Match/no-match and k-NN analysis tests have been carried out to confirm the diagnosis of PCS. The prediction model has given 100% sensitivity and specificity. Receiver operating characteristics (ROC) has been plotted for the prediction model, and the area under the curve (AUC) is obtained as 1. The E-nose technique is found to be working well for PCS diagnosis. Our study suggests that the breath analysis using E-nose can be used as a point-of-care diagnosis of PCS.Trial registrationBreath samples were collected from the Kasturba Hospital, Manipal. Ethical clearance was obtained from the Institutional Ethics Committee, Kasturba Medical College, Manipal (IEC 60/2021, 13/01/2021) and Indian Council of Medical Research (ICMR) (CTRI/2021/02/031357, 06/02/2021) Government of India; trials were prospectively registered.

Accuracy of the Electronic Nose Breath Tests in Clinical Application: A Systematic Review and Meta-Analysis.

(1) Background: An electronic nose applies a sensor array to detect volatile biomarkers in exhaled breath to diagnose diseases. The overall diagnostic accuracy remains unknown. The objective of this review was to provide an estimate of the diagnostic accuracy of sensor-based breath tests for the diagnosis of diseases. (2) Methods: We searched the PubMed and Web of Science databases for studies published between 1 January 2010 and 14 October 2021. The search was limited to human studies published in the English language. Clinical trials were not included in this review. (3) Results: Of the 2418 records identified, 44 publications were eligible, and 5728 patients were included in the final analyses. The pooled sensitivity was 90.0% (95% CI, 86.3-92.8%, I2 = 47.7%), the specificity was 88.4% (95% CI, 87.1-89.5%, I2 = 81.4%), and the pooled area under the curve was 0.93 (95% CI 0.91-0.95). (4) Conclusion: The findings of our review suggest that a standardized report of diagnostic accuracy and a report of the accuracy in a test set are needed. Sensor array systems of electronic noses have the potential for noninvasiveness at the point-of-care in hospitals. Nevertheless, the procedure for reporting the accuracy of a diagnostic test must be standardized.

The potential of volatile organic compounds-based breath analysis for COVID-19 screening: a systematic review & meta-analysis.

COVID-19 is a major problem with an increasing incidence and mortality. The discovery of Volatile Organic Compounds (VOCs) based on breath analysis offers a reliable, rapid, and affordable screening method. This study examined VOC-based breath analysis diagnostic performance for SARS-COV-2 infection compared to RT-PCR. A systematic review was conducted in 8 scientific databases based on the PRISMA guideline. Original English studies evaluating human breaths for COVID-19 screening and mentioning sensitivity and specificity value compared to RT-PCR were included. Six studies were included with a total of 4093 samples from various settings. VOCs-based breath analysis had the cumulative sensitivity of 98.2% (97.5% CI 93.1%-99.6%) and specificity of 74.3% (97.5% CI 66.4%-80.9%). Subgroup analysis on chemical analysis (GC-MS) and pattern recognition (eNose) revealed higher sensitivity in the eNose group. VOC-based breath analysis shows high sensitivity and promising specificity for COVID-19 public screening.

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

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.

Applying the electronic nose for pre-operative SARS-CoV-2 screening.

BACKGROUND: Infection with SARS-CoV-2 causes corona virus disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19-positive and negative persons based on volatile organic compounds (VOCs) analysis. METHODS: Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, the presence of SARS-CoV-2-specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine learning and used for pattern recognition. The result is a value between - 1 and + 1, indicating the infection probability. RESULTS: 219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96. CONCLUSIONS: The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery.

The Smell in COVID-19 Infection: Diagnostic Opportunities.

BACKGROUND: There is a high prevalence of olfaction changes, especially in the early presentation, in COVID-19 patients. The mechanisms through which the virus leads to anosmia/hyposmia is still not fully understood. However, olfaction changes could be used as an indication for testing or quarantine. Screening for infections and other diseases by recognizing volatile organic compounds (VOCs) has been previously conducted. Hence, if the coronavirus infection also results in VOCs excretion, physicians could "smell" the virus by using electronic noses. We conducted a literature review on olfaction changes and the COVID-19. Our results suggest that these changes could be used an indication for early testing, even as an isolated symptom. We propose that the electronic nose be used as a future screening tool, especially in agglomeration spaces such as airports, for screening for the COVID-19 infection.