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Article

Advancing COVID-19 Detection in a University Environment: Comprehensive Validation and Longitudinal Analysis of High-Throughput Breathalyzer Technology

1
Department of Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
2
UHealth Information Technology, University of Miami, Miami, FL 33136, USA
3
Terahertz Group Ltd., Herzliya 4672517, Israel
4
Office of the Provost, University of Miami, Miami, FL 33136, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Microbiol. 2025, 5(2), 35; https://doi.org/10.3390/applmicrobiol5020035
Submission received: 20 February 2025 / Revised: 11 March 2025 / Accepted: 14 March 2025 / Published: 22 March 2025

Abstract

:
The COVID-19 pandemic has underscored the need for efficient and non-invasive diagnostic tools for early detection and management. This study evaluated the TERA breath analyzer (TERA.Bio®), a Real-Time High-Throughput Breathalyzer, for SARS-CoV-2 detection. It aimed to validate and implement the TERA.Bio® for the detection of SARS-CoV-2 within a university population compared to RT-qPCR testing. Conducted at the University of Miami, this observational study consisted of two phases: a validation phase and a longitudinal monitoring phase, using cross-sectional and prospective cohort designs, respectively. Participants, including symptomatic individuals and those in close contact with confirmed cases, underwent simultaneous testing with the TERA.Bio® and mid-nasal swab RT-qPCR tests. The study evaluated TERA.Bio®’s accuracy, sensitivity, specificity, and its role for surveillance. A total of 27,445 breath samples were analyzed through the TERA.Bio®. In the validation phase, the TERA.Bio® demonstrated a sensitivity of 64% and a specificity of 85.1%. Longitudinal monitoring revealed no significant correlation between unclear TERA.Bio® results and smoking status. The TERA.Bio® is a viable tool for COVID-19 screening in university environments, providing rapid, cost-effective, apt extensive screening and monitoring in a dense academic setting. Its non-invasive nature, high throughput, and electronic health system compatibility make it an essential addition to existing COVID-19 diagnostic strategies. This study highlights the critical role of innovative diagnostic tools in pandemic management and suggests potential applications of TERA.Bio® technology in broader public health scenarios.

1. Introduction

The emergence and rapid global spread of the novel coronavirus disease 2019 (COVID-19) posed an unprecedented challenge to public health and healthcare systems worldwide [1]. The highly contagious nature of the virus, SARS-CoV-2, necessitated the development of efficient diagnostic tools for early detection and containment [2].
These challenges underscore the importance of developing rapid, accessible, and reliable alternatives for the detection of COVID-19. Despite ongoing efforts, the pandemic continues to be a global public health challenge, with many countries maintaining restrictions to mitigate transmission and protect healthcare services from overwhelming new infections.
Furthermore, the asymptomatic spread of COVID-19 among individuals, particularly within university populations, has raised significant concerns [3]. Asymptomatic carriers can unknowingly contribute to the transmission of the virus, making early and frequent testing of paramount importance in preventing outbreaks within educational institutions [4].
Multiple sample collection methods have been explored for the detection of infected individuals, including posterior pharyngeal swabs, mid-nasal swabs, and saliva [5,6,7,8]. However, all these samples must undergo analysis using the current gold standard for virus detection, real-time polymerase chain reactions (RT-qPCRs). Despite its accuracy, RT-qPCR testing has limitations, including the time required for processing and the need for specialized laboratory facilities [9]. The analysis is typically not conducted at the collection site, necessitating costly transportation of the samples to a certified laboratory. Moreover, RT-qPCR testing is highly accurate for known pathogens that are targeted for amplification, but it may not always detect emerging variants of concern. To be effective as a screening test, the frequency of the testing and speed of reporting are minimally related to the individual sensitivity of the test [10,11].
Therefore, there is a pressing need to implement efficient and easily deployable testing methods capable of identifying both symptomatic and asymptomatic cases, facilitating timely interventions and reducing the risk of viral transmission.
In response to these imperatives, this study aimed to validate the use of a Real-Time High-Throughput Breathalyzer, the TERA breath analyzer (TERA.Bio®, Herzliya, Israel), as an innovative and accessible tool for the detection of SARS-CoV-2 within a university population. In the first section of our study, we focus on the validation of the TERA compared to the RT-qPCR method, which is considered the gold standard for SARS-CoV-2 detection, as recommended by the World Health Organization [12]. Our goal was to assess the TERA.Bio®’s accuracy, sensitivity, and specificity in detecting SARS-CoV-2 in breath samples, establishing its reliability as a diagnostic tool.
In the second part of our study, we transitioned from the validation phase to the implementation phase, highlighting the critical aspect of monitoring and follow-up. Effective containment of COVID-19 within the university setting necessitates not only accurate initial detection but also ongoing monitoring. This is particularly vital in a dynamic academic environment where individuals constantly interact, creating opportunities for viral spread. We aimed to establish a longitudinal follow-up system to track the presence of the virus within the university community over time, regardless of symptomatic or asymptomatic status. By doing so, we intended to contribute valuable insights into the dynamics of COVID-19 transmission within the university setting, assess the effectiveness of mitigation strategies, and facilitate prompt responses to potential outbreaks.

2. Methods

2.1. Study Design and Population

This real-world, observational study consisted of two distinct phases: the validation phase and the longitudinal monitoring (surveillance) phase. The validation phase employed a cross-sectional design, whereas the longitudinal monitoring phase followed a prospective cohort approach.
The sample size was determined through a convenience sampling strategy, which first included individuals exhibiting symptoms of COVID-19 and those who had close contact with symptomatic or known positive students at the University of Miami, located in the Coral Gables or downtown Miami medical campus. The sample included a broad demographic range in terms of age, gender, health status, and campus locations, reflecting the diversity of the University of Miami population, and the study’s comprehensive temporal span across different pandemic phases further supports its representativeness. These individuals provided their consent for simultaneous testing using the Tera BioStation breath analyzer (TERA.Bio®, Herzliya, Israel) and mid-nasal swab RT-qPCR tests. The validation phase underwent testing during the Spring Semester, which commenced later than usual due to the pandemic, spanning from 8 February 2021 to 31 May 2021.
The longitudinal monitoring cohort was assembled during the Fall Semester, from 15 August 2021 to 20 December 2021, during which 8796 TERA.Bio® tests were administered. This monitoring period coincided with two significant waves of SARS-CoV-2 in Miami. The first wave, occurring primarily with the DELTA variant, transpired from 10 July 2021 to 26 October 2021, while the second wave featured the OMICRON variant of SARS-CoV-2 and extended from 1 December 2021 to 10 February 2022. In the period of longitudinal monitoring, a total of 3668 subjects actively participated in TERA.Bio® tests conducted twice weekly. Selection for participation was based on their residence hall on campus, and it was extended to encompass other students and staff who preferred not to undergo mid-nasal RT-qPCR testing. The monitoring testing was a key component in the comprehensive COVID-mitigation strategy by enabling the isolation of individuals with SARS-CoV-2 infection. Moreover, it significantly contributed to the prompt and thorough contact tracing and quarantine procedures within the campus community.
Data collection encompassed a range of variables, including demographic and clinical parameters, anthropometric measurements, and the medical history of comorbidities. Additionally, participants were required to provide self-reported information regarding current and prior COVID infections, as well as any symptoms associated with COVID. The list of symptoms under investigation included cough, sore throat, shortness of breath, non-allergy related runny nose, loss or reduction of smell or taste, muscular pain, headache, fatigue, chills, sleepiness, and gastrointestinal (GI) symptoms such as vomiting, diarrhea, and abdominal pain.
For analytical purposes, the symptoms were grouped into four categories: “none”, “1 symptom”, “2–5 symptoms”, or “6 or more symptoms”.

2.2. Ethics

This study received approval from the University of Miami Institutional Review Board (IRB), along with the necessary approvals from ancillary committees responsible for overseeing the research. Informed consent was obtained from all participants involved in the studies (Clinical Study IDs 20201211 and 20210115).

2.3. Tera BioStation Breath Analyzer and RT-qPCR Sampling Protocol

During the pandemic, the University of Miami implemented a comprehensive COVID-19 surveillance program, mandating mid-nasal swab RT-qPCR testing for all campus residents. Students residing on campus were given the option to participate in a novel breath testing program using the Tera BioStation breath analyzer (TERA.Bio®, Herzliya, Israel). Those opting out of the breath test were required to undergo the standard nasal swab testing. Additionally, a select group of non-residential students, mandated for on-campus presence, were also included in the nasal swab testing protocol, with ten individuals opting for the breath test alternative.
An electronic consent (e-consent) process was utilized, leveraging the Research Electronic Data Capture (REDCap) system and electronic medical records for efficient and secure data management. This system ensured validated data capture and integrated seamlessly with the university’s existing health record systems [13,14].
Students consenting to the breath test were sent automated text reminders through the patient portal, prompting bi-weekly test scheduling. Non-compliance triggered follow-up reminders, with persistent non-compliance reported to the Dean of Students.
For operational efficiency, three testing sites were strategically located near student residential halls. The entire testing process, including consent registration, sample collection, and result notification, was streamlined to take approximately 10 min. Upon arrival at the testing site, students were provided with a QR-coded TeraTube (TERA.Bio®, Herzliya, Israel). for sample collection, instructed to blow into the tube thrice, seal it, and hand it over for immediate analysis. The TERA.Bio® delivered results within three minutes, categorizing them as “clear”, “not clear”, or “retest”.
Results were promptly recorded in the electronic medical record system and communicated to students via SMS. In cases where results were “not clear” or “retest”, students were directed to undergo an additional FDA-authorized COVID-19 test. A “not clear” breath test result necessitated an immediate on-site mid-nasal RT-qPCR test for a conclusive diagnosis.
Support for the testing process was available around the clock through a dedicated phone line and email, staffed by the university’s screening team. The location of testing sites and the streamlined protocol were designed to ensure minimal disruption to student routines while maintaining efficient and effective campus-wide surveillance.

2.4. Terahertz (THz) Spectroscopy Overview

Terahertz (THz) spectroscopy is a powerful technique used to investigate the properties of materials using electromagnetic waves in the terahertz frequency range (0.1 to 10 THz). This technology is particularly valuable in biomedical imaging, material characterization, and non-destructive testing, due to its ability to penetrate various materials without causing ionization. Several THz spectroscopic techniques exist, each differing in their operational principles and resolution.
Terahertz Time-Domain Spectroscopy (THz-TDS) operates by measuring the time delay of terahertz pulses as they pass through a sample. This technique typically offers a resolution of up to 10 GHz and is widely used for detailed analysis of electromagnetic properties such as absorption coefficient, refractive index, and dielectric constant.
Frequency-Domain Spectroscopy (FDS) measures the absorption and phase shift of continuous-wave terahertz radiation as a function of frequency. FDS systems generally achieve resolutions better than 50 MHz and are particularly useful for the detailed analysis of material properties over a wide range of frequencies.
Terahertz Emission Spectroscopy detects and measures the THz radiation emitted by a sample when excited by a laser. This method operates with a resolution in the range of a few GHz and is commonly used for analyzing the emission characteristics of semiconductor materials and devices.

2.5. THz Technology in the Tera BioStation T101

THz radiation has an expanding range of applications, which utilizes its special properties.
One can find this spectral range useful for many applications, such as general imaging; non-destructive-tests (NDT); biomedical imaging; spectroscopy; defense and national security; remote sensing; and even communication. The underlying technology of the TERA breath analyzer, which employs Terahertz Impedance Spectroscopy for detecting biological nanoparticles, has been previously described [15].
A major advantage of THz radiation is that it is characterized by low energy; hence, it does not cause the ionization of biomolecules. Moreover, this property of low photon energy enables us to interact with many materials via several mechanisms, and it opens a window to many interactions. These mechanisms include phonons and weak bond vibrations and, thus, are unique to chemical and physical compositions.
This characteristic of non-ionized low photon energy has a significant potential in medical diagnosis and medical treatments. It has unique bio-fingerprints such as VOCs, viruses, bacteria, etc.
In this study, the Tera BioStation T101 employs Frequency-Domain Spectroscopy (FDS) as its core technology. The FDS system in the Tera BioStation T101 is designed to capture and analyze the spectral signatures of biomaterials by measuring the absorption and phase shift of terahertz waves as they interact with the breath samples. This allows for the identification of complex patterns associated with the presence of SARS-CoV-2, making it an effective tool for non-invasive COVID-19 detection.

2.6. System Description

The breath analysis tests were carried out via a portable electronic station named the Tera BioStation T101. The endogenic and exogenic bio-materials of respiratory aerosols are collected in a single use accessory device named a TeraTube and then inspected by the Tera BioStation T101.
Using integrated THz technology, the Tera BioStation T101 diagnostic platform, through its AI algorithm, can distinguish between healthy and suspected infected patients. Its advantage is in the combination of spectral signatures of biomaterials (using the spectroscopy system performed by the THz system) in the THz spectral bandwidth and the uniq AI algorithm, which was developed by Tera Ltd.
The main components of the “BioStation” include two distributed-feedback class IIIB lasers, electric temperature control units, a control unit, a power unit, and a set of two photo-mixers. The two laser beams are combined, creating a beating phenomenon. This beating then modulates a photocurrent at a tuned THz frequency, illuminating the photo-mixers. The THz beam travels from the photo-mixer transmitter through the “Teratube” sample, and the signal is received at the photo-mixer receiver. This signal is then analyzed and associated with the algorithm to provide an answer.

2.7. Statistical Analyses

Continuous variables are presented as either mean ± standard deviation (SD) or median and interquartile intervals (25th–75th), depending on the distribution of the data. Statistical comparisons for these variables were performed using the t-test or rank sum test, as deemed appropriate. For the assessment of associations among categorical variables, the χ2 test or Fisher’s exact test was employed. Sensitivity and specificity were defined as the ability of a test to accurately identify individuals who were either SARS-CoV-2 positive (True Positive rate) or SARS-CoV-2 negative (True Negative rate). To evaluate the validity of the TERA.Bio® test with RT-qPCR tests as the gold standard, the Area under the Curve (AUC) of the Receiver Operating Characteristics (ROCs) was calculated. The Youden index (J) was calculated for each threshold value of the TERA.Bio® test when compared with the gold standard RT-qPCR. The threshold that maximizes the Youden index was identified as the optimal cut-off point for the TERA.Bio® breath analyzer. Adjusted ROC curves were constructed using a logistic regression procedure, treating the state variable as the dependent variable and including selected covariates. Predicted probabilities were saved as a variable for subsequent use in ROC analysis. Covariates used in this model comprised sex, age, and current and past smoking status.
Statistical significance was defined as a p-value less than 0.05 for all analyses. The data were analyzed using IBM SPSS® v28.0 (New York, NY, USA).

2.8. Role of Funders

The funding and the support for this study were jointly provided by the University of Miami and the Tera Grant Initiative. The funders played a crucial role in facilitating the research by providing the necessary financial support and resources. However, they had no involvement in the study design, collection, analysis, or interpretation of data. The preparation of this manuscript, including the writing and decision to submit it for publication, was conducted independently by the research team. All aspects of the research were performed with full academic freedom, and the funders did not influence the study outcomes or the conclusions drawn by the researchers. The integrity and objectivity of the research were maintained throughout, ensuring that the findings and conclusions presented in this study are solely those of the authors.

3. Results

3.1. Validation Study

During the validation phase, a total of 1041 individuals were assessed, comprising 984 students located at the Coral Gables campus and 57 students situated at the medical campus. The median (25th–75th) age of the participants was 20 (19–21) years, with 59.8% of them being female (n = 623), with a mean Body Mass Index (BMI) of 23.7 ± 4.9 kg/m2. Among the 1041 subjects, 3.2% were active smokers (n = 33), and 3.9% were former smokers. Table 1 provides a detailed breakdown of the participants’ characteristics and the frequency of COVID-related symptoms. Out of the 1041 subjects, 594 underwent both TERA.Bio® and RT-qPCR sampling simultaneously. Out of the 1041 subjects, 447 participants preferred not to undergo mid-nasal RT-qPCR testing and were included in the monitoring phase. Positive results for SARS-CoV2 with RT-qPCR were significantly associated with students located at the medical campus (F = 287.14; DF = 1; p < 0.001) and male gender (F = 7.453; DF = 1; p = 0.006). The mean age of individuals testing positive for SARS-CoV2 RT-qPCR was 56.1 ± 23.3 years, in contrast to 21.8 ± 6.3 years for those who tested negative for SARS-CoV2 with RT-qPCR (p < 0.001).

3.2. Diagnostic Accuracy of Tera BioStation Breath Analyzer

The diagnostic accuracy of TERA.Bio® tests was evaluated using ROC curve analysis, which indicated that the TERA.Bio® test can be a valuable tool for detecting SARS-CoV2 in positive subjects, with an AUC of 0.745 (95% CI: 0.633–0.858; p < 0.0001; see Figure 1). The diagnostic performance of the TERA.Bio® breath analyzer was further evaluated by calculating the Youden index for each threshold value in comparison with the gold standard RT-qPCR. The maximum Youden index observed was 0.492, corresponding to a threshold value that provided a sensitivity of 64% and a specificity of 85.1%. This threshold represents the optimal balance between correctly identifying positive and negative cases, indicating the effectiveness of the TERA.Bio® test in the studied population.
We conducted additional subgroup analyses to assess the accuracy of TERA.Bio® tests with respect to sex. Among female subjects, the TERA.Bio® demonstrated a sensitivity of 77.8% and a specificity of 85.3%, resulting in an AUC of 0.815 (95% CI: 0.633–0.858; p < 0.0001). In contrast, male subjects showed a sensitivity of 53.3% and a specificity of 85.1%, with an AUC of 0.692 (95% CI: 0.536–0.849; p = 0.013). These results suggest that male subjects exhibited a higher rate of false-positive results compared to female subjects (see Figure 2).
We further examined the performance of TERA.Bio® tests while considering adjustments for current and past smoker status, age, and sex. This analysis revealed a higher accuracy with an AUC of 0.923 (95% CI 0.843–1.0; p < 0.0001; see Figure 3).
When analyzing the association between TERA.Bio® test results and the presence of reported symptoms, we observed a significant association between the presence of one or more symptoms and a positive TERA.Bio® test result (p < 0.001).
To assess the impact of virus strains and seasonal variations, we analyzed the number of positive cases over time. Table 2 shows the temporal distribution of TERA.Bio® test results, highlighting potential correlations with the emergence of different SARS-CoV-2 variants.

3.3. Longitudinal Monitoring Study

During the longitudinal monitoring study, a total of 27,445 valid breath samples were collected and subsequently analyzed using the Tera BioStation. These samples were collected repeatedly from 2578 subjects. The mean age of this cohort was 20.34 ± 1.76 years, with 60.5% of the participants being female (n = 1560). The number of samples collected per individual ranged from 1 to 50, with a median value of 10. The median follow-up duration was 63 days (see Table 3). The longer the duration of follow-up increased with the frequency of testing for each individual, as illustrated in Figure 4.
Out of the 27,445 samples, 26,972 presented clear (negative) and 473 presented not-clear (positive) results. For the subjects with not-clear results, mid-nasal RT-qPCR tests were performed to 413 out of the 473, all of which returned negative results for SARS-CoV2. No positive results were reported for the RT-qPCR tests conducted during the longitudinal monitoring phase. No significant association was observed between TERA.Bio® not-clear test results and smoking status (p = 0.057).
We conducted a cost-effectiveness comparison between the TERA.Bio® test and RT-qPCR. Our findings revealed that TERA.Bio® testing not only offers a shorter processing and reporting time but also comes at a lower cost, as indicated in Table 4.
The contingency measure employed involved isolating individuals who received a TERA.Bio® not-clear result, keeping them separated from others until further testing could be conducted. This measure was taken as a precautionary step to help prevent the potential spread of the virus. Students who were not cleared by TERA.Bio® test results were placed in self-isolation until the PCR testing was available.
All subjects received, twice a week, automated text reminders to self-schedule their twice weekly appointment for the breath test through the patient portal. Failure to register for the test prompted a message to the student to do so; failure to respond to the prompt was reported to the Dean of Students. Assistance with the entire process was available 24/7 by phone staffed by a university-based screening team or by email.

4. Discussion

This study employed two approaches, involving both a validation phase and a prospective cohort for longitudinal monitoring (surveillance), encompassing a total of 3619 participants. The results from this validation study showed that the Real-Time High-Throughput Breathalyzer is a reliable tool for detecting SARS-CoV-2 in individuals within a university population. It exhibited an overall sensitivity of 64% and a specificity of 85.1%. These performance characteristics align with those reported in previous studies that focused on rapid and self-tests utilizing antigen-based diagnostics, especially when compared to self-testing conducted on the same day as RT-qPCR sampling [16].
The first breath analyzer approved under an emergency use authorization by the US FDA for COVID-19 was the InspectIR COVID-19 Breathalyzer employing gas chromatography and mass spectroscopy (GC–MS). It showed high accuracy in detecting COVID-19 and its Omicron variant through breath analysis [17]. Since then, other studies have also demonstrated the potential of breath analysis in detecting SARS-CoV-2 infections among patients with moderate to severe cases who are receiving treatment in hospitals or intensive care units [18,19,20]. Grassin-Delyle et al. [21] also published a study in EBioMedicine, where they analyzed distinct VOCs in the breath of mechanically ventilated adults suffering from COVID-19. This breath signature was then contrasted with a control group of ventilated patients who had acute respiratory distress syndrome but not COVID-19. Recently, the feasibility of using UV spectroscopy on exhaled breath was demonstrated, specifically analyzing carbonyl VOCs, to distinguish COVID-19 positive individuals, highlighting the potential of breath analysis as a rapid, non-invasive diagnostic tool for the disease [22].
Additionally, breath analysis had shown promise in identifying individuals with mild symptoms who are infected with the virus [23]. However, the technology employed in these studies involves GC–MS. It is important to note that TERA.Bio® technology and GC–MS are distinct analytical techniques. While GC–MS focuses on the specific VOCs present in breath samples, the TERA.Bio® operates by identifying patterns and features associated with COVID-19 materials (complexes) within the spectrum. In comparison to liquid chromatography–mass spectrometry, the TERA.Bio® breath analyzer offers several advantages, including rapid results (usually in under 5 min from registration to result), the ability to operate without the need for medical personnel, and cost-effectiveness. These attributes make TERA.Bio® technology a practical choice for widespread population testing and monitoring during a pandemic.
A breath analyzer, when used in conjunction with rigorous contact tracing, swift isolation, and quarantine procedures, and under the guidance of a collaborative leadership structure that facilitates quick decision making and adaptation to emerging data, plays a vital role in curbing the spread of SARS-CoV-2 within a university environment [15].
These components form the foundation for ongoing monitoring and operational feasibility, even in the face of worsening local transmission throughout the semester. In the case of COVID-19 transmission, the surveillance phase applied in this study encompassed a comprehensive strategy that involved testing, contact tracing, and the efficient management of isolation and quarantine measures, effectively controlling COVID-19 transmission within the university setting.
One limitation of the study was the difficulty in seamlessly integrating the results of the TERA breath analyzer with individual participant records. The TERA machine, a stand-alone device, lacks connectivity with other systems and does not retain any participant information, posing a significant challenge in data management and analysis. To overcome this, the study utilized the electronic medical record system’s capability to scan the QR code on the testing tube specimen. This approach enabled the researchers to link each breath test specimen directly to the respective medical record.
During the study period, vaccination efforts against COVID-19 were in full swing across the United States, including in Miami-Dade County. By mid-2021, approximately 60–70% of the eligible population in the region had received at least one dose of a COVID-19 vaccine. Within the study cohort, a significant number of participants were vaccinated, particularly among university staff and older students, which may have influenced the results. Vaccination has been shown to reduce the severity of COVID-19 symptoms and lower viral load, potentially affecting the sensitivity of detection methods such as the TERA.Bio® breath analyzer. However, the influence of vaccination on the breathalyzer’s performance was not directly assessed in this study. It is worth noting that vaccinated individuals may exhibit lower concentrations of viral biomarkers in their breath, which could lead to reduced sensitivity in non-invasive tests like the TERA.Bio®. Further studies are recommended to explore the interaction between vaccination status and breath analysis results, particularly in the context of new and emerging variants of concern.
A significant strength of this study is its large sample size, which offered a comprehensive representation of the university population, thereby providing valuable insights into the efficacy of the TERA breath analyzer in a real-world environment characterized by high population density, thereby underscoring the utility of this approach in managing pandemic situations in similar settings.
The TERA.Bio® breath analyzer also offers significant advantages in terms of cost-effectiveness and operational efficiency, particularly in large-scale testing environments like universities. One of its key benefits is its non-invasive nature, requiring only a simple breath sample, which reduces the risk of cross-contamination and eliminates the need for physical contact, unlike Rapid Antigen Tests (RATs). The system provides rapid results within 5–10 min from sample collection, significantly faster than the 25 h typically required for RT-qPCR tests and often quicker than some RATs. Additionally, the direct cost per test for the TERA.Bio® system is approximately $5.00, which is considerably lower than the $30.00 per test for mid-nasal RT-qPCR testing. This cost advantage becomes particularly important in scenarios where frequent testing is necessary on a large scale, such as in university settings with thousands of tests conducted weekly. The ease of use is another critical factor, as the TERA.Bio® system does not require specialized personnel for test administration, allowing students and staff to self-administer the breath test with minimal training. These factors combine make the TERA.Bio® breath analyzer a practical and cost-effective choice for continuous surveillance in large populations, especially where rapid decision making and immediate isolation of potential cases are essential.

5. Conclusions

This study effectively utilized the TERA breath analyzer (TERA.Bio®) to validate and monitor COVID-19, demonstrating its practicality in a densely populated and dynamic university environment. The TERA.Bio® was confirmed to be a rapid, cost-effective, and non-invasive testing method, offering significant benefits for large-scale screening and monitoring, crucial for controlling the spread of the virus in educational institutions. This study contributes to the evolving field of COVID-19 diagnostic strategies, underscoring the importance of innovative approaches in managing pandemic situations effectively.

Author Contributions

Conceptualization, K.H., R.E.W. and R.A.-N.; methodology, M.S., J.Y. and R.E.W.; software, G.W., J.C.P.; validation, J.R.N.L., G.W. and J.C.P.; formal analysis, J.R.N.L.; investigation, K.H., M.S., E.K. and R.E.W.; resources, E.G., R.A.-N., I.B.-G. and R.E.W.; data curation, G.W., J.C.P. and J.R.N.L.; writing—original draft preparation, K.H., J.R.N.L. and R.E.W.; writing—review and editing, J.R.N.L., R.M. and R.E.W.; visualization, J.R.N.L. and R.E.W.; supervision, R.E.W.; project administration, K.H., E.K. and R.E.W.; funding acquisition, R.E.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by a gift to the University of Miami from Sullivan Land Services Co (SLSCO), Galveston, Texas, USA , and Tera Grant Initiative.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the University of Miami (Clinical Study IDs 20201211 and 20210115, approved on 28 July 2021).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).

Acknowledgments

We express our sincere gratitude to both the University of Miami and the Tera Grant Initiative for their unwavering support and commitment to public health and innovative research. Their combined efforts significantly contributed to the success of this study. We also appreciate the participation of our faculty, staff, and students and acknowledge the dedicated teams that enabled this research.

Conflicts of Interest

The authors E.G., R.A.-N., and I.B.-G. are employed by Terahertz Group Ltd. This affiliation did not alter our adherence to all the journal’s policies on sharing data and materials. There are no other conflicts of interest to declare. All other authors have declared that no competing interests exist, and there are no personal, financial, or professional affiliations or relationships that could be perceived as potential conflicts of interest influencing the research process, analysis, or interpretation of the findings.

Abbreviations

COVID-19Coronavirus Disease 2019
SARS-CoV-2Severe Acute Respiratory Syndrome Coronavirus 2
ARDSAcute Respiratory Distress Syndrome
VOCsVolatile Organic Compounds
RT-qPCRReal-Time Polymerase Chain Reaction
TERA.Bio®TERA Breath Analyzer
FDAFood and Drug Administration
GC–MSGas Chromatography and Mass Spectroscopy
THzTerahertz
BMIBody Mass Index
AUCArea Under the Curve
ROCReceiver Operating Characteristic
REDCapResearch Electronic Data Capture
NDTNon-Destructive Testing

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Figure 1. Receiver operating characteristic (ROC) curve illustrating the diagnostic accuracy of the TERA.Bio® test compared to RT-qPCR as the gold standard (n = 594). The curve represents the trade-off between sensitivity and specificity at various threshold levels. Area under the curve (AUC) and 95% confidence intervals (CI) are provided to assess performance reliability.
Figure 1. Receiver operating characteristic (ROC) curve illustrating the diagnostic accuracy of the TERA.Bio® test compared to RT-qPCR as the gold standard (n = 594). The curve represents the trade-off between sensitivity and specificity at various threshold levels. Area under the curve (AUC) and 95% confidence intervals (CI) are provided to assess performance reliability.
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Figure 2. Receiver operating characteristic (ROC) curves stratified by sex, comparing the diagnostic accuracy of the TERA.Bio® test to RT-qPCR as the gold standard. The figure highlights differences in sensitivity and specificity between male (n = 210) and female (n = 369) participants. Area under the curve (AUC) values and 95% confidence intervals (CI) are indicated for each subgroup.
Figure 2. Receiver operating characteristic (ROC) curves stratified by sex, comparing the diagnostic accuracy of the TERA.Bio® test to RT-qPCR as the gold standard. The figure highlights differences in sensitivity and specificity between male (n = 210) and female (n = 369) participants. Area under the curve (AUC) values and 95% confidence intervals (CI) are indicated for each subgroup.
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Figure 3. Receiver operating characteristic (ROC) curves adjusted for sex, age, and smoking status, demonstrating the diagnostic performance of the TERA.Bio® test relative to RT-qPCR (n = 594). The adjustments aim to control for potential confounding factors, providing a more accurate assessment of the test’s effectiveness. Area under the curve (AUC) and 95% confidence intervals (CI) are reported.
Figure 3. Receiver operating characteristic (ROC) curves adjusted for sex, age, and smoking status, demonstrating the diagnostic performance of the TERA.Bio® test relative to RT-qPCR (n = 594). The adjustments aim to control for potential confounding factors, providing a more accurate assessment of the test’s effectiveness. Area under the curve (AUC) and 95% confidence intervals (CI) are reported.
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Figure 4. Scatter plot illustrating the distribution of breath samples collected over the follow-up period, with the y-axis representing the number of samples and the x-axis indicating days of follow-up. Each point corresponds to a single sample collection event, providing insight into testing frequency and participant compliance.270.
Figure 4. Scatter plot illustrating the distribution of breath samples collected over the follow-up period, with the y-axis representing the number of samples and the x-axis indicating days of follow-up. Each point corresponds to a single sample collection event, providing insight into testing frequency and participant compliance.270.
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Table 1. Baseline characteristics of participants during the validation study.
Table 1. Baseline characteristics of participants during the validation study.
Subjects‘ CharacteristicsN = 1041
Age, years mean (SD)23 (9.3)
Female, n (%) 623 (59.8)
BMI, kg/m2 mean (SD)23.7 (4.9)
Smoker, n (%)33 (3.2)
Former Smoker, n (%)41 (3.9)
Obesity, yes, n (%)56 (5.4)
Kidney disease, n (%)3 (0.3)
Blood-related disease, n (%)4 (0.4)
Active cancer, n (%)6 (0.6)
Type 1 diabetes, n (%)5 (0.5)
Type 2 diabetes, n (%)6 (0.6)
COVID related symptoms, n (%)6 (0–91)
           None867 (87%)
           1 symptom54 (5.4)
           2–5 symptoms61 (6.1)
           6 or more symptoms16 (1.6)
Table 2. Temporal trends and virus strains.
Table 2. Temporal trends and virus strains.
Time PeriodPositive TERA.Bio®Positive RT-qPCRNot-Clear Results
February–May 202112010515
June–August 202190855
September–December 202115014010
Table 3. Baseline and follow-up characteristics of participants during the longitudinal monitoring study.
Table 3. Baseline and follow-up characteristics of participants during the longitudinal monitoring study.
Subjects‘ CharacteristicsN = 2578
Age, years mean (SD)20.34 (1.76)
Female, n (%) 1560 (60.5)
Ethnicity, n (%)
           Hispanic/Latino251 (9.7)
           Non-Hispanic/Latino1119 (43.4)
           Refused 24 (0.9)
           Unknown1184 (45.9)
N of samples, median (min–max)10 (1–50)
Time of follow-up, days, median (min–max)63 (0–297)
Time between sampling, days, mean (min–max)6 (0–91)
Table 4. Comparison of TERA breath analyzer and mid-nasal swab PCR testing for SARS-CoV2.
Table 4. Comparison of TERA breath analyzer and mid-nasal swab PCR testing for SARS-CoV2.
TERA.Bio® TestingMid-Nasal RT-qPCR Testing
Capacity of tests/24 h480280
Processing Time *5–10 min7 h
Collection to Notification Time5–10 min25 h
Direct Cost/Test$5.00$30.00
* Excluding transportation to laboratory.
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Hirani, K.; Lemos, J.R.N.; Suarez, M.; Mittal, R.; Yern, J.; Wicks, G.; Pena, J.C.; Gabbai, E.; Aharonov-Nadborny, R.; Ben-Giat, I.; et al. Advancing COVID-19 Detection in a University Environment: Comprehensive Validation and Longitudinal Analysis of High-Throughput Breathalyzer Technology. Appl. Microbiol. 2025, 5, 35. https://doi.org/10.3390/applmicrobiol5020035

AMA Style

Hirani K, Lemos JRN, Suarez M, Mittal R, Yern J, Wicks G, Pena JC, Gabbai E, Aharonov-Nadborny R, Ben-Giat I, et al. Advancing COVID-19 Detection in a University Environment: Comprehensive Validation and Longitudinal Analysis of High-Throughput Breathalyzer Technology. Applied Microbiology. 2025; 5(2):35. https://doi.org/10.3390/applmicrobiol5020035

Chicago/Turabian Style

Hirani, Khemraj, Joana R. N. Lemos, Maritza Suarez, Rahul Mittal, Jannet Yern, Giselle Wicks, Julio C. Pena, Eran Gabbai, Regina Aharonov-Nadborny, Iko Ben-Giat, and et al. 2025. "Advancing COVID-19 Detection in a University Environment: Comprehensive Validation and Longitudinal Analysis of High-Throughput Breathalyzer Technology" Applied Microbiology 5, no. 2: 35. https://doi.org/10.3390/applmicrobiol5020035

APA Style

Hirani, K., Lemos, J. R. N., Suarez, M., Mittal, R., Yern, J., Wicks, G., Pena, J. C., Gabbai, E., Aharonov-Nadborny, R., Ben-Giat, I., Kobetz, E., & Weiss, R. E. (2025). Advancing COVID-19 Detection in a University Environment: Comprehensive Validation and Longitudinal Analysis of High-Throughput Breathalyzer Technology. Applied Microbiology, 5(2), 35. https://doi.org/10.3390/applmicrobiol5020035

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