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.

Volatile Organic Compound Exhaled Breath Sensing

This book chapter presents a broad overview of the application of nanotechnology in the biomedical area, exemplified by the application of several gas sensors (electrochemical sensors, piezoelectric sensors, optical, chemoresistive, metal oxide sensors, surface acoustic wave sensors) and focusing on the study of volatile organic compounds (VOCs) in exhaled breath for the screening of diseases of worldwide interest such as breast cancer, lung cancer, COVID-19, post COVID-19 syndrome, colorectal cancer, prostate cancer, diabetes, chronic obstructive disease, among others. This document aims to provide the state of the art in disruptive technologies based on nanosensors, especially electronic noses and the advances and perspectives in this field. The present work represents an important tool for researchers who are in the field of the development of sensing disruptive technologies for the study of VOCs in biological matrices (i.e., exhaled breath). Thus, the application of gas sensors has proven to be feasible in the biomedical area and a promising area within the diagnosis of communicable and non-communicable diseases, to be applied in POC settings, clinics, hospitals, doctors' offices, and especially in-field applications for less-favored populations where they lack the minimum resources to achieve universal health coverage. © 2023 Elsevier Ltd. All rights reserved

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.

Identification of Respiratory Diseases and Diabetes by Non-invasive Method Using IoT

The objective of this paper is to identify respiratory diseases such as Asthma, Covid-19, pulmonary disease, and diabetes from the human breath odor using a non-invasive method. For detecting diseases using a non-invasive method, temperature sensor (to identify body temperature), pulse oximeter sensor (to identify blood oxygen level and heartbeat rate), and acetone sensor (to identify respiratory diseases from human breath odor) with Arduino ATMega328 microcontroller unit (MCU) were used. If the temperature is greater than 37.2 C, the heartbeat rate is greater than 100 bpm, and the oxygen level is less than 92% Covid-19 will be detected. If the oxygen level is less than 95% the heartbeat rate is at (100–125) bpm, and the temperature is at 36.1–37 C, asthma will be detected. If the heart rate is greater than 86 bpm, the temperature at 36.1–37 C, the oxygen level at 92–97% and the acetone level at (354–496) ppm, diabetes will be detected. If the oxygen level is less than 92% the temperature at 36.1–37 C, and the heartbeat rate is greater than 110 bpm, the pulmonary disease will be identified. After disease detection, suggestions will be provided to the patients based on their health reports. Finally, suggested medicines will be sent to the patient's registered mobile phones by connecting node MCU with blynk using IoT technology. The results will be stored and the patients can compare their health conditions for future analysis. The traditional method of laboratory tests is considered to consume more time. In our method, the duration of the detection process is less and the results help to identify health problems at early stages and predict diseases quickly compared to the traditional method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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.

Metabolic Changes of VOCs in Human Caused by Wearing Masks: A Preliminary Study Based on Electronic Nose

Wearing masks has been generally recommended to reduce the spreading of COVID-19. However, little is known about its effects on metabolic VOC changes in human body. To explore how the duration of wearing masks influences VOC metabolism in the human body, the essay used a self-developed electronic nose to analyse exhaled breath samples from 10 healthy individuals in this study. Firstly, polytetrafluoroethylene sampling bags are used to collect breath samples after volunteers wearing masks for 1h, 2h, 3h, 4h, and 5h. Secondly, data pre-processing, including baseline calibration and normalization are carried out. Thirdly, the study used LDA for dimensionality reduction on the original data to extract 4 features. Fourthly, differences in the length of time of wearing masks are analysed. Then, 4 algorithms were applied for cluster analysis based on extracted features. Moreover, 3 supervised classification algorithms were used to recognize the duration of wearing masks. Finally, multi-dimensional linear regression is used to study the possibility of predicting the duration of wearing masks based on breath signals acquired through electronic noses. As a result, the first feature extracted by LDA significantly differs from each other in the duration of wearing masks (p<0.05). Cluster analysis results show that the optimal internal parameters Adjusted Rand Index, Adjusted Mutual Information, Homogeneity and V-measure reach 80.2%, 81.5%, 83.5% and 83.7% respectively. Using 5-fold cross-validation on the K nearest neighbour classification model, the best accuracy of recognizing durations of wearing a mask reaches 88%. R-square of multi-dimensional linear regression reaches 92.5%, which shows excellent fitting performance. It can be concluded that the VOC metabolism of the human may change with the duration of wearing masks. Further, "breath prints” obtained by electronic nose may have the potential to predict the effective time and even the quality of masks. © 2022 SPIE.

Volatile Organic Compound Exhaled Breath Sensing

This book chapter presents a broad overview of the application of nanotechnology in the biomedical area, exemplified by the application of several gas sensors (electrochemical sensors, piezoelectric sensors, optical, chemoresistive, metal oxide sensors, surface acoustic wave sensors) and focusing on the study of volatile organic compounds (VOCs) in exhaled breath for the screening of diseases of worldwide interest such as breast cancer, lung cancer, COVID-19, post COVID-19 syndrome, colorectal cancer, prostate cancer, diabetes, chronic obstructive disease, among others. This document aims to provide the state of the art in disruptive technologies based on nanosensors, especially electronic noses and the advances and perspectives in this field. The present work represents an important tool for researchers who are in the field of the development of sensing disruptive technologies for the study of VOCs in biological matrices (i.e., exhaled breath). Thus, the application of gas sensors has proven to be feasible in the biomedical area and a promising area within the diagnosis of communicable and non-communicable diseases, to be applied in POC settings, clinics, hospitals, doctors’ offices, and especially in-field applications for less-favored populations where they lack the minimum resources to achieve universal health coverage.

Forecasting the Post-Pandemic Effects of the SARS-CoV-2 Virus Using the Bullwhip Phenomenon Alongside Use of Nanosensors for Disease Containment and Cure.

The COVID-19 pandemic has the tendency to affect various organizational paradigm alterations, which civilization hasyet to fully comprehend. Personal to professional, individual to corporate, and across most industries, the spectrum of transformations is vast. Economically, the globe has never been more intertwined, and it has never been subjected to such widespread disruption. While many people have felt and acknowledged the pandemic's short-term repercussions, the resultant paradigm alterations will certainly have long-term consequences with an unknown range and severity. This review paper aims at acknowledging various approaches for the prevention, detection, and diagnosis of the SARS-CoV-2 virus using nanomaterials as a base material. A nanostructure is a material classification based on dimensionality, in proportion to the characteristic diameter and surface area. Nanoparticles, quantum dots, nanowires (NW), carbon nanotubes (CNT), thin films, and nanocomposites are some examples of various dimensions, each acting as a single unit, in terms of transport capacities. Top-down and bottom-up techniques are used to fabricate nanomaterials. The large surface-to-volume ratio of nanomaterials allows one to create extremely sensitive charge or field sensors (electrical sensors, chemical sensors, explosives detection, optical sensors, and gas sensing applications). Nanowires have potential applications in information and communication technologies, low-energy lightning, and medical sensors. Carbon nanotubes have the best environmental stability, electrical characteristics, and surface-to-volume ratio of any nanomaterial, making them ideal for bio-sensing applications. Traditional commercially available techniques have focused on clinical manifestations, as well as molecular and serological detection equipment that can identify the SARS-CoV-2 virus. Scientists are expressing a lot of interest in developing a portable and easy-to-use COVID-19 detection tool. Several unique methodologies and approaches are being investigated as feasible advanced systems capable of meeting the demands. This review article attempts to emphasize the pandemic's aftereffects, utilising the notion of the bullwhip phenomenon's short-term and long-term effects, and it specifies the use of nanomaterials and nanosensors for detection, prevention, diagnosis, and therapy in connection to the SARS-CoV-2.

Urinary volatile recognition for COVID-19 diagnosis

Volatile organic compounds (VOCs) of urine samples of Covid-19 patients and healthy controls have been collected and analyzed with a gas chromatograph-mass spectrometer (GC-MS) and the actual version of the Tor Vergata electronic nose (E-Nose). This untargeted metabolic investigation leads to a set of 5 discriminative VOCs with 84.38%, 94.44%, 71.43% for accuracy, sensitivity, and specificity. The E- Nose has sniffed Covid-19 subjects at 92.1 %, 100%, 85%, as the accuracy, sensitivity, and specificity respectively. © 2022 IEEE.

Application of intelligent sensory technology in the authentication of alcoholic beverages

Alcoholic beverages play an important role in social gatherings and the consumption of alcohol drinks keep increasing worldwide in recent years, especially during the COVID-19 pandemic months. The authentication of alcoholic beverage is usually evaluated by trained panels or chromatography analysis. Over the last few decades, intelligent sensory technology (IST) that imitate the human sensory organs have been developed for quality control and authentication of alcoholic beverages. The artificial sensing system consist of arrays of sensors with cross-sensitivity and various pattern recognition methods, which can be used to discriminate or classify the samples based on the detection requirements. Application of IST on wine authenticity have been extensively studied, however, application of IST in authentication of other alcoholic beverages lacks of systemic study. This paper firstly describes the basic mechanism of current IST instruments and then summarizes the applications of IST in alcoholic beverages authenticity assessments, including discrimination of varietal and geographical origins, detection of frauds and adulterations, discrimination of years of aging, distinction of brands and types, aroma analysis, detection of spoilage and off-flavors, and monitoring of the production process. The potential applications and future development of IST in the brewing industry are also discussed.

An Intelligent Electronic Nose for the assistance of Anosmic-Patients

Anosmic people's inability to detect any odors almost always results in unfavorable outcomes. Failure to identify gas leaks or dangerous substances is seen as a threat to their safety. However, as a result of the COVID-19 Pandemic, the number of anosmic patients is steadily increasing. In this paper we propose a system assists anosmic patients in recognizing hazards that they cannot smell. This revolutionary system detects gas leaks, smoke, and early fires, as well as dangerous substances, automatically. With the aid of an array of gas sensors and different machine learning algorithms the, E-nose can identify six distinct smells. At last, if any hazardous gas spread occurs, the system fires alert message specifics the identified gas or event. We succeeded in achieving F1-score of 98 % for Support Vector Machine (SVM), logistic regression, and Decision Tree. While K-nearest Neighbors and Random Forest scored 100%. © 2022 IEEE.

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.

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

BACKGROUND: Non-invasive, bedside diagnostic tools are extremely important for tailo ring the management of respiratory failure patients. The use of electronic noses (ENs) for exhaled breath analysis has the potential to provide useful information for phenotyping different respiratory disorders and improving diagnosis, but their application in respiratory failure patients remains a challenge. We developed a novel measurement apparatus for analysing exhaled breath in such patients. METHODS: The breath sampling apparatus uses hospital medical air and oxygen pipeline systems to control the fraction of inspired oxygen and prevent contamination of exhaled gas from ambient Volatile Organic Compounds (VOCs) It is designed to minimise the dead space and respiratory load imposed on patients. Breath odour fingerprints were assessed using a commercial EN with custom MOX sensors. We carried out a feasibility study on 33 SARS-CoV-2 patients (25 with respiratory failure and 8 asymptomatic) and 22 controls to gather data on tolerability and for a preliminary assessment of sensitivity and specificity. The most significant features for the discrimination between breath-odour fingerprints from respiratory failure patients and controls were identified using the Boruta algorithm and then implemented in the development of a support vector machine (SVM) classification model. RESULTS: The novel sampling system was well-tolerated by all patients. The SVM differentiated between respiratory failure patients and controls with an accuracy of 0.81 (area under the ROC curve) and a sensitivity and specificity of 0.920 and 0.682, respectively. The selected features were significantly different in SARS-CoV-2 patients with respiratory failure versus controls and asymptomatic SARS-CoV-2 patients (p < 0.001 and 0.046, respectively). CONCLUSIONS: the developed system is suitable for the collection of exhaled breath samples from respiratory failure patients. Our preliminary results suggest that breath-odour fingerprints may be sensitive markers of lung disease severity and aetiology.

Detection of COVID-19 infection based on electronic nose technique: preliminary study

The coronavirus COVID-19 pandemic have reached almost every country in the world and caused a global health crisis. It is necessary to detect COVID-19 with fast and accurate diagnosis method in order to prevent the rapid spread of Covid-19. This paper presents a preliminary study of using electronic nose (e-nose) technology for detection of COVD-19 infection. In this experiment, the human exhaled breaths of healthy volunteers, asymptomatic and symptomatic COVID-19 patients were collected with commercial face masks for 5 minutes followed by the measurement with an e-nose machine in a closed system. The COVID-19 positivity was confirmed by RT-PCR method. According to the experiment, the odor intensity of human exhaled breath can be described with the total sensing response value. The exhaled breath of COVID-19 infected patients show higher odor intensity than the healthy volunteers (control). The Principal Component Analysis (PCA) shows the classification of three data groups;healthy volunteers, COVID-19 infected patients and unclassified people. For the unclassified cases, the medical record has shown that these people have been subjected either to some respiratory diseases or just recovered from COVID-19 infection. From these preliminary results, e-nose technology and its measurement proto-cols can be considered as a viable tool for COVID-19 rapid detection. © 2022 IEEE.

Development of a COVID-19 University-Based Clinic in Indonesia:A Pilot Project of The Gadjah Mada Electronic Nose Center

BACKGROUND: The clinic development of COVID-19 screening is essential during the pandemic. AIM: This study aimed to explore and elaborate the development process of the Gadjah Mada Electronic Nose (GeNose) Center as a pilot project for a COVID-19 university-based clinic in Indonesia. METHODS: A narrative and explorative study was conducted. Under the university platform, we initiated the GeNose center through training, simulation, and debriefing. Identification of team member recruitment, location, and apparatus development were described using the retrospective approach. RESULTS: Fifty-one team members were recruited, including person in charge, verifiers, administrative staffs, hotline team, security staffs, and janitors. Standard operating procedures, service system, and safety measures were developed to maintain the quality. Services include the application of COVID-19 protocols, registration and confirmation, education for using the air bag, collecting the air sample, and analysis of samples using the GeNose machine. CONCLUSION: The GeNose center, a model for screening test, provides services for the screening of COVID-19. © 2022 Anggi Lukman Wicaksana, Nurul Dyah Kusumawati, Ella Permatasari Wibowo, Hera Nirwati.

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.

Portable Tools for COVID-19 Point-of-Care Detection: A Review.

Recently, several methods for SARS-CoV-2 detection have been developed to obtain rapid, portable, cheap, and easy-to-use diagnostic tools. This review paper summarizes and discusses studies on the development of point-of-care devices for SARS-CoV-2 diagnosis with comparisons between them from several aspects. Various detection methods of the recently developed portable COVID-19 biosensor will be presented in this review. The discussion is divided into four major classifications based on the target biomarkers of SARS-CoV-2, such as antibodies, nucleic acids, antigens, and metabolic products. An overview of the potential development for future study is also provided. Moreover, basic knowledge of biosensors is also explained for tutoring the implementation of theory into the research of COVID-19 biosensors. This review paper is aimed to provide a tutorial by collecting the information on the development of a point-of-care device for SARS-CoV-2 detection to provide information for further research and propose the new COVID-19 portable diagnostic tool.

Chemometric analysis of the global pattern of volatile organic compounds in the exhaled breath of patients with COVID-19, post-COVID and healthy subjects. Proof of concept for post-COVID assessment.

The objective of this research was to evaluate the application of an electronic nose and chemometric analysis to discriminate volatile organic compounds between patients with COVID-19, post-COVID syndrome and controls in exhaled breath samples. A cross-sectional study was performed on 102 exhaled breath samples, 42 with COVID-19, 30 with the post-COVID syndrome and 30 control subjects. Breath-print analysis was performed by the Cyranose 320 electronic nose with 32 sensors. Group data were evaluated by Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA), and Support Vector Machine (SVM), and the test's diagnostic power was evaluated through a Receiver Operaring Characteristic curve(ROC curve). The results of the chemometric analysis indicate in the PCA a 97.6% (PC1 = 95.9%, PC2 = 1.0%, PC3 = 0.7%) of explanation of the variability between the groups by means of 3 PCs, the CDA presents a 100% of correct classification of the study groups, SVM a 99.4% of correct classification, finally the PLS-DA indicates an observable separation between the groups and the 12 sensors that were related. The sensitivity, specificity of post-COVID vs. controls value reached 97.6% (87.4%-99.9%) and 100% (88.4%-100%) respectively, according to the ROC curve. As a perspective, we consider that this technology, due to its simplicity, low cost and portability, can support strategies for the identification and follow-up of post-COVID patients. The proposed classification model provides the basis for evaluating post-COVID patients; therefore, further studies are required to enable the implementation of this technology to support clinical management and mitigation of effects.