An inexpensive UV-LED photoacoustic based real-time sensor-system detecting exhaled trace-acetone

In this research we present a low-cost system for breath acetone analysis based on UV-LED photoacoustic spectroscopy. We considered the end-tidal phase of exhalation, which represents the systemic concentrations of volatile organic compounds (VOCs) - providing clinically relevant information about the human health. This is achieved via the development of a CO2-triggered breath sampling system, which collected alveolar breath over several minutes in sterile and inert containers. A real-time mass spectrometer is coupled to serve as a reference device for calibration measurements and subsequent breath analysis. The new sensor system provided a 3σ detection limit of 8.3 ppbV and an NNEA of 1.4E-9 Wcm-1Hz-0.5. In terms of the performed breath analysis measurements, 12 out of 13 fell within the error margin of the photoacoustic measurement system, demonstrating the reliability of the measurements in the field.

Determination of the two-compartment model parameters of exhaled HCN by fast negative photoionization mass spectrometry

Breath exhaled hydrogen cyanide (HCN) has been identified to be associated with several respiratory diseases. Accurately distinguishing the concentration and release rate of different HCN sources is of great value in clinical research. However, there are still significant challenges due to the high adsorption and low concentration characteristics of exhaled HCN. In this study, a two-compartment kinetic model method based on negative photoionization mass spectrometry was developed to simultaneously determine the kinetic parameters including concentrations and release rates in the airways and alveoli. The influences of the sampling line diameter, length, and temperature on the response time of the sampling system were studied and optimized, achieving a response time of 0.2 s. The negative influence of oral cavity-released HCN was reduced by employing a strategy based on anatomical lung volume calculation. The calibration for HCN in the dynamic range of 0.5-100 ppbv and limit of detection (LOD) at 0.3 ppbv were achieved. Subsequently, the experiments of smoking, short-term passive smoking, and intake of bitter almonds were performed to examine the influences of endogenous and exogenous factors on the dynamic parameters of the model method. The results indicate that compared with steady-state concentration measurements, the kinetic parameters obtained using this model method can accurately and significantly reflect the changes in different HCN sources, highlighting its potential for HCN-related disease research.

Ammonia detection: A pathway towards potential point-of-care diagnostics

Invasive methods such as blood collection and biopsy are commonly used for testing liver and kidney function, which are painful, time-consuming, require trained personnel, and may not be easily accessible to people for their routine checkup. Early diagnosis of liver and kidney diseases can prevent severe symptoms and ensure better management of these patients. Emerging approaches such as breath and sweat analysis have shown potential as non-invasive methods for disease diagnosis. Among the many markers, ammonia is often used as a biomarker for the monitoring of liver and kidney functions. In this review we provide an insight into the production and expulsion of ammonia gas in the human body, the different diseases that could potentially use ammonia as biomarker and analytical devices such as chemiresistive gas sensors for non-invasive monitoring of this gas. The review also provides an understanding into the different materials, doping agents and substrates used to develop such multifunctional sensors. Finally, the current challenges and the possible future trends have been discussed.

A comprehensive meta-analysis and systematic review of breath analysis in detection of COVID-19 through Volatile organic compounds

BACKGROUND

The COVID-19 pandemic had profound global impacts on daily lives, economic stability, and healthcare systems. Diagnosis of COVID-19 infection via RT-PCR was crucial in reducing spread of disease and informing treatment management. While RT-PCR is a key diagnostic test, there is room for improvement in the development of diagnostic criteria. Identification of volatile organic compounds (VOCs) in exhaled breath provides a fast, reliable, and economically favorable alternative for disease detection.

METHODS

This meta-analysis analyzed the diagnostic performance of VOC-based breath analysis in detection of COVID-19 infection. A systematic review of twenty-nine papers using the grading criteria from Newcastle-Ottawa Scale (NOS) and PRISMA guidelines was conducted.

RESULTS

The cumulative results showed a sensitivity of 0.92 (95 % CI, 90 %-95 %) and a specificity of 0.90 (95 % CI 87 %-93 %). Subgroup analysis by variant demonstrated strong sensitivity to the original strain compared to the Omicron and Delta variant in detection of SARS-CoV-2 infection. An additional subgroup analysis of detection methods showed eNose technology had the highest sensitivity when compared to GC-MS, GC-IMS, and high sensitivity-MS.

CONCLUSION

Overall, these results support the use of breath analysis as a new detection method of COVID-19 infection.

Enhancing diagnosis of benign lesions and lung cancer through ensemble text and breath analysis: a retrospective cohort study

Early diagnosis of lung cancer (LC) can significantly reduce its mortality rate. Considering the limitations of the high false positive rate and reliance on radiologists' experience in computed tomography (CT)-based diagnosis, a multi-modal early LC screening model that combines radiology with other non-invasive, rapid detection methods is warranted. A high-resolution, multi-modal, and low-differentiation LC screening strategy named ensemble text and breath analysis (ETBA) is proposed that ensembles radiology report text analysis and breath analysis. In total, 231 samples (140 LC patients and 91 benign lesions [BL] patients) were screened using proton transfer reaction-time of flight-mass spectrometry and CT screening. Participants were randomly assigned to a training set and a validation set (4:1) with stratification. The report section of the radiology reports was used to train a text analysis (TA) model with a natural language processing algorithm. Twenty-two volatile organic compounds (VOCs) in the exhaled breath and the prediction results of the TA model were used as predictors to develop the ETBA model using an extreme gradient boosting algorithm. A breath analysis model was developed based on the 22 VOCs. The BA and TA models were compared with the ETBA model. The ETBA model achieved a sensitivity of 94.3%, a specificity of 77.3%, and an accuracy of 87.7% with the validation set. The radiologist diagnosis performance with the validation set had a sensitivity of 74.3%, a specificity of 59.1%, and an accuracy of 68.1%. High sensitivity and specificity were obtained by the ETBA model compared with radiologist diagnosis. The ETBA model has the potential to provide sensitivity and specificity in CT screening of LC. This approach is rapid, non-invasive, multi-dimensional, and accurate for LC and BL diagnosis.

Lung cancer detection in perioperative patients' exhaled breath with nanomechanical sensor array

INTRODUCTION

Breath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning.

METHODS

Patients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included. Exhaled breaths were collected just before surgery and about one month after surgery, and analyzed using an MSS array. The array had 12 channels with various receptor materials and provided 12 waveforms from a single exhaled breath sample. Boxplots of the perioperative changes in the expiratory waveforms of each channel were generated and Mann-Whitney U test were performed. An optimal lung cancer prediction model was created and validated using machine learning.

RESULTS

Sixty-six patients were enrolled of whom 57 were included in the analysis. Through the comprehensive analysis of the entire dataset, a prototype model for predicting lung cancer was created from the combination of array five channels. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.809, 0.830, 0.807, 0.806, and 0.812, respectively.

CONCLUSION

Breath analysis with MSS and machine learning with careful control of both samples and measurement conditions provided a lung cancer prediction model, demonstrating its capacity for non-invasive screening of lung cancer.

Breath metabolomics for diagnosis of acute respiratory distress syndrome

BACKGROUND

Acute respiratory distress syndrome (ARDS) poses challenges in early identification. Exhaled breath contains metabolites reflective of pulmonary inflammation.

AIM

To evaluate the diagnostic accuracy of breath metabolites for ARDS in invasively ventilated intensive care unit (ICU) patients.

METHODS

This two-center observational study included critically ill patients receiving invasive ventilation. Gas chromatography and mass spectrometry (GC-MS) was used to quantify the exhaled metabolites. The Berlin definition of ARDS was assessed by three experts to categorize all patients into "certain ARDS", "certain no ARDS" and "uncertain ARDS" groups. The patients with "certain" labels from one hospital formed the derivation cohort used to train a classifier built based on the five most significant breath metabolites. The diagnostic accuracy of the classifier was assessed in all patients from the second hospital and combined with the lung injury prediction score (LIPS).

RESULTS

A total of 499 patients were included in this study. Three hundred fifty-seven patients were included in the derivation cohort (60 with certain ARDS; 17%), and 142 patients in the validation cohort (47 with certain ARDS; 33%). The metabolites 1-methylpyrrole, 1,3,5-trifluorobenzene, methoxyacetic acid, 2-methylfuran and 2-methyl-1-propanol were included in the classifier. The classifier had an area under the receiver operating characteristics curve (AUROCC) of 0.71 (CI 0.63-0.78) in the derivation cohort and 0.63 (CI 0.52-0.74) in the validation cohort. Combining the breath test with the LIPS does not significantly enhance the diagnostic performance.

CONCLUSION

An exhaled breath metabolomics-based classifier has moderate diagnostic accuracy for ARDS but was not sufficiently accurate for clinical use, even after combination with a clinical prediction score.

Towards detection of cancer biomarkers in human exhaled air by transfer-learning-powered analysis of odor-evoked calcium activity in rat olfactory bulb

Detection of volatile organic compounds in exhaled air is a promising approach to non-invasive and scalable gastric cancer screening. This work proposes a new approach for the detection of volatile organic compounds by analyzing odor-evoked calcium responses in the rat olfactory bulb. We estimate the feasibility of gastric cancer biomarker detection added to the exhaled air of healthy participants. Our detector consists of a convolutional encoder and a similarity-based classifier over encoder outputs. To minimize overfitting on a small available training set, we involve a pre-training where the encoder is trained on synthetic data representing spatiotemporal patterns similar to real calcium responses in the olfactory bulb. We estimate the classification accuracy of exhaled air samples by matching their encodings with encodings of calibration samples of two classes: 1) exhaled air and 2) a mixture of exhaled air with the cancer biomarker. On our data, the accuracy increased from 0.68 on real data up to 0.74 if pre-training on synthetic data is involved. Our work is focused on proving the feasibility of proposed new approach rather than on comparing its efficiency with existing methods. Such detection is often performed with an electronic nose, but its output becomes unstable over time due to a sensor drift. In contrast to the electronic nose, rats can robustly detect low concentrations of biomarkers over lifetime. The feasibility of gastric cancer biomarker detection in exhaled air by bio-hybrid system is shown. Pre-training of neural models for images analysis increases the accuracy of detection.

Standard operating procedure to reveal prostate cancer specific volatile organic molecules by infrared spectroscopy

The growing number of prostate cancer cases is a real concern in modern society. Over 1.4 million new cases and about 400 thousand (>26%) deaths were registered worldwide in 2020 due to prostate cancer. The high mortality rate of prostate cancer is due to the lack of reliable early detection of the disease. Till now the most reliable diagnosis of cancer is tissue biopsy, which is an invasive process. A non-invasive or minimally invasive technique could lead to a diagnostic tool that will allow for saving or prolonging the lifespan of millions of lives. Metabolite-based diagnostics may have a better chance of early cancer detection. However, reliable detection techniques need to be developed. Infrared spectroscopy based gaseous-biofluid holds great promise towards the development of non-invasive diagnostics. A pilot study based on breath analysis by infrared spectroscopy showed promising results in distinguishing prostate cancer patients from healthy volunteers. Details of the spectral metabolic analysis are presented.

Very Rapid Improvement in Extended Nitric Oxide Parameters Is Associated With Clinical and Functional Improvement in Patients With Chronic Rhinosinusitis With Nasal Polyps Treated With Dupilumab

BACKGROUND AND OBJECTIVE

Dupilumab, an anti-IL-4 receptor a monoclonal antibody, was recently approved for the treatment of chronic rhinosinusitis with nasal polyps (CRSwNP) and moderate-to-severe asthma. Onset of its clinical effects is rapid. CRSwNP is characterized by extended type 2 inflammatory involvement that can be assessed using extended nitric oxide analysis. We investigated whether dupilumab was associated with a rapid improvement in extended nitric oxide parameters, lung function, and clinical outcomes in patients with CRSwNP.

METHODS

Consecutive patients with CRSwNP and an indication for dupilumab were evaluated for extended nitric oxide analysis (exhaled, FeNO; bronchial, JawNO; alveolar, CalvNO; nasal, nNO) and lung function 15 and 30 days after initiation of treatment and for clinical outcomes (nasal polyps score [NPS], quality of life questionnaires, visual analog scale [VAS] for the main symptoms, and the Asthma Control Test [ACT]) 30 days after initiation of treatment.

RESULTS

We enrolled 33 patients. All extended nitric oxide and lung function parameters improved significantly after 15 days of treatment, remaining stable at 30 days. Scores on the NPS, VAS for the main RSwNP symptoms, quality of life questionnaires, and the ACT improved significantly 30 days after initiation of treatment.

CONCLUSION

Dupilumab is associated with very rapid improvement in type 2 inflammation in all airway areas. This is associated with improved lung function and clinical parameters in patients with CRSwNP.

Water interference in the chromatographic analysis of exhaled breath samples: Challenges and mitigation strategies

This study demonstrates the adverse effects of water in exhaled breath samples on the accuracy of breath biomarker analysis when using gas chromatography. The presence of water in exhaled breath significantly modifies the retention times and peak areas of compounds, particularly for low-boiling, early eluting compounds. To tackle this issue, a two-step approach is introduced. The process begins with thorough desorption of the sorbent tube using a high split ratio and a short analysis duration, followed by a secondary analysis of the same tube. The efficacy of the new, straightforward approach was illustrated using humid breath samples and 57 compound standard mixture. This study highlights the importance of proper sample pretreatment and analysis to ensure reliable and accurate results in clinical research.

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.

Mask device as a new wearable sampler for breath analysis: what can we expect in the future?

Human exhaled breath is becoming an attractive clinical source as it is foreseen to enable noninvasive diagnosis of many diseases. Because mask devices can be used for efficiently filtering exhaled substances, mask-wearing has been required in the past few years in daily life since the unprecedented COVID-19 pandemic. In recent years, there is a new development of mask devices as new wearable breath samplers for collecting exhaled substances for disease diagnosis and biomarker discovery. This paper attempts to identify new trends in mask samplers for breath analysis. The couplings of mask samplers with different (bio)analytical approaches, including mass spectrometry (MS), polymerase chain reaction (PCR), sensor, and others for breath analysis, are summarized. The developments and applications of mask samplers in disease diagnosis and human health are reviewed. The limitations and future trends of mask samplers are also discussed.

A sub-ppbv-level Acetone and Ethanol Quantum Cascade Laser Based Photoacoustic Sensor - Characterization and Multi-Component Spectra Recording in Synthetic Breath

Trace gas analysis in breath is challenging due to the vast number of different components. We present a highly sensitive quantum cascade laser based photoacoustic setup for breath analysis. Scanning the range between 8263 and 8270 nm with a spectral resolution of 48 pm, we are able to quantify acetone and ethanol within a typical breath matrix containing water and CO₂. We photoacoustically acquired spectra within this region of mid-infra-red light and prove that those spectra do not suffer from non-spectral interferences. The purely additive behavior of a breath sample spectrum was verified by comparing it with the independently acquired single component spectra using Pearson and Spearman correlation coefficients. A previously presented simulation approach is improved and an error attribution study is presented. With a 3σ detection limit of 6.5 ppbv in terms of ethanol and 250 pptv regarding acetone, our system is among the best performing presented so far.

Proteomic characteristics and diagnostic potential of exhaled breath particles in patients with COVID-19

BACKGROUND

SARS-CoV-2 has been shown to predominantly infect the airways and the respiratory tract and too often have an unpredictable and different pathologic pattern compared to other respiratory diseases. Current clinical diagnostical tools in pulmonary medicine expose patients to harmful radiation, are too unspecific or even invasive. Proteomic analysis of exhaled breath particles (EBPs) in contrast, are non-invasive, sample directly from the pathological source and presents as a novel explorative and diagnostical tool.

METHODS

Patients with PCR-verified COVID-19 infection (COV-POS, n = 20), and patients with respiratory symptoms but with > 2 negative polymerase chain reaction (PCR) tests (COV-NEG, n = 16) and healthy controls (HCO, n = 12) were prospectively recruited. EBPs were collected using a "particles in exhaled air" (PExA 2.0) device. Particle per exhaled volume (PEV) and size distribution profiles were compared. Proteins were analyzed using liquid chromatography-mass spectrometry. A random forest machine learning classification model was then trained and validated on EBP data achieving an accuracy of 0.92.

RESULTS

Significant increases in PEV and changes in size distribution profiles of EBPs was seen in COV-POS and COV-NEG compared to healthy controls. We achieved a deep proteome profiling of EBP across the three groups with proteins involved in immune activation, acute phase response, cell adhesion, blood coagulation, and known components of the respiratory tract lining fluid, among others. We demonstrated promising results for the use of an integrated EBP biomarker panel together with particle concentration for diagnosis of COVID-19 as well as a robust method for protein identification in EBPs.

CONCLUSION

Our results demonstrate the promising potential for the use of EBP fingerprints in biomarker discovery and for diagnosing pulmonary diseases, rapidly and non-invasively with minimal patient discomfort.

Mask assistance to colorimetric sniffers for detection of Covid-19 disease using exhaled breath metabolites

According to World Health Organization reports, large numbers of people around the globe have been infected or died for Covid-19 due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Researchers are still trying to find a rapid and accurate diagnostic method for revealing infected people by low viral load with the overriding goal of effective diagnostic management. Monitoring the body metabolic changes is known as an effective and inexpensive approach for the evaluation of the infected people. Here, an optical sniffer is introduced to detect exhaled breath metabolites of patients with Covid-19 (60 samples), healthy humans (55 samples), and cured people (15 samples), providing a unique color pattern for differentiation between the studied samples. The sniffer device is installed on a thin face mask, and directly exposed to the exhaled breath stream. The interactions occurring between the volatile compounds and sensing components such as porphyrazines, modified organic dyes, porphyrins, inorganic complexes, and gold nanoparticles allowing for the change of the color, thus being tracked as the sensor responses. The assay accuracy for the differentiation between patient, healthy and cured samples is calculated to be in the range of 80%-84%. The changes in the color of the sensor have a linear correlation with the disease severity and viral load evaluated by rRT-PCR method. Interestingly, comorbidities such as kidney, lung, and diabetes diseases as well as being a smoker may be diagnosed by the proposed method. As a powerful detection device, the breath sniffer can replace the conventional rapid test kits for medical applications.

Mask assistance to colorimetric sniffers for detection of Covid-19 disease using exhaled breath metabolites

According to World Health Organization reports, large numbers of people around the globe have been infected or died for Covid-19 due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Researchers are still trying to find a rapid and accurate diagnostic method for revealing infected people by low viral load with the overriding goal of effective diagnostic management. Monitoring the body metabolic changes is known as an effective and inexpensive approach for the evaluation of the infected people. Here, an optical sniffer is introduced to detect exhaled breath metabolites of patients with Covid-19 (60 samples), healthy humans (55 samples), and cured people (15 samples), providing a unique color pattern for differentiation between the studied samples. The sniffer device is installed on a thin face mask, and directly exposed to the exhaled breath stream. The interactions occurring between the volatile compounds and sensing components such as porphyrazines, modified organic dyes, porphyrins, inorganic complexes, and gold nanoparticles allowing for the change of the color, thus being tracked as the sensor responses. The assay accuracy for the differentiation between patient, healthy and cured samples is calculated to be in the range of 80%-84%. The changes in the color of the sensor have a linear correlation with the disease severity and viral load evaluated by rRT-PCR method. Interestingly, comorbidities such as kidney, lung, and diabetes diseases as well as being a smoker may be diagnosed by the proposed method. As a powerful detection device, the breath sniffer can replace the conventional rapid test kits for medical applications.

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.

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 registration

Breath 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.

Effect of household air pollutants on the composition of exhaled breath characterized by solid-phase microextraction and needle-trap devices

Exposure to household air pollutants is becoming a serious environmental health risk. Various methods can be applied to assess humans' exposure status to indoor pollutants, with breath monitoring being among the best options. Breath sampling is fast and non-invasive, and contains compounds that can be used as markers for evaluating exposure length and estimating internal concentrations of pollutants. However, the distribution of compounds between gas and droplets in breath samples represents one of the key challenges associated with this analytical method. In this work, a needle-trap device (NTD) was prepared by packing the needle with a porous filter, divinyl benzene, and Carboxen to enable the exhaustive capture of both droplet-bound and gaseous components. Furthermore, fiber-based solid-phase microextraction (SPME) was also applied to extract compounds from only the gas phase to distinguish this portion of analytes from the total concentration in the sample. Dynamic, real-time breath sampling was enabled via a new sampling tube equipped with 2 one-way valves, which was specially designed for this work. Both methods provided satisfactory reproducibility, repeatability, and sensitivity, with detection limits as low as 0.05 ng mL^-1. To investigate the real-world applicability of the proposed devices, breath samples were obtained from volunteers who had been exposed to candle and incense smoke and aerosol sprays, or had smoked cannabis. The results revealed the high concentration of organic air pollutants in inhaled air (maximum of 215 ng mL^-1) and exhaled breath (maximum of 14.4 ng mL^-1) and a correlation between the components in inhaled air and exhaled breath. Significantly, the findings further revealed that the developed NTD has enhanced breath-sample determinations, especially for polar compounds, which tend to remain trapped in breath droplets.

Volatile Organic Compounds in Human Breath

A comprehensive analysis of volatile organic compounds (VOCs) from the exhaled breath sample is termed as breathomics. Breath samples are a complex mixture composed of a multitude of VOCs and other molecules. The analysis of total VOCs in exhaled breath provides a promising tool for the diagnosis of many diseases because it enables the observation of biochemical processes in the body in a non-invasive way. VOCs are produced in various physiological and pathophysiological conditions thus making it a potential biomarker for several diseases.

Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis

BACKGROUND AND AIMS

In this work, breath samples from clinically stable bronchiectasis patients with and without bronchial infections by Pseudomonas Aeruginosa- PA) were collected and chemically analysed to determine if they have clinical value in the monitoring of these patients.

MATERIALS AND METHODS

A cohort was recruited inviting bronchiectasis patients (25) and controls (9). Among the former group, 12 members were suffering PA infection. Breath samples were collected in Tedlar bags and analyzed by e-nose and Gas Chromatography-Mass Spectrometry (GC-MS). The obtained data were analyzed by chemometric methods to determine their discriminant power in regards to their health condition. Results were evaluated with blind samples.

RESULTS

Breath analysis by electronic nose successfully separated the three groups with an overall classification rate of 84% for the three-class classification problem. The best discrimination was obtained between control and bronchiectasis with PA infection samples 100% (CI_95%: 84-100%) on external validation and the results were confirmed by permutation tests. The discrimination analysis by GC-MS provided good results but did not reach proper statistical significance after a permutation test.

CONCLUSIONS

Breath sample analysis by electronic nose followed by proper predictive models successfully differentiated between control, Bronchiectasis and Bronchiectasis PA samples.

Detection of COPD and Lung Cancer with electronic nose using ensemble learning methods

BACKGROUND AND AIMS

The chemical gas sensor array based electronic-nose (e-nose) devices with machine learning algorithms can detect and differentiate expelled breath samples of patients with various respiratory ailments and controls. It is by the recognition of levels and variations of volatile organic compounds (VOC) in the exhaled air. Here, we aimed to differentiate chronic obstructive pulmonary disease (COPD) and lung cancer from controls.

MATERIALS AND METHODS

This work presents the details of the developed e-nose system, selection of the study subjects, exhaled breath sampling method and detection, and the data analysis algorithms. The developed device is tested in 199 participants including 93 controls, 55 COPD patients, and 51 lung cancer patients. The main advantage of the device is robustness and portability and cost-effectiveness.

RESULTS

In the training phase and model validation phase, the ensemble learning method XGBoost outperformed the other two models. In the prediction of lung cancer, XGBoost method attained a classification accuracy of 79.31%. In COPD prediction also the same method had given the better results with 76.67% accuracy.

CONCLUSION

The e-nose system developed with TGS gas sensors was portable, low cost, and gave a rapid response. It has been demonstrated that the VOC profiles of patients with pulmonary diseases and healthy controls are different and hence the e-nose system can be used as a potential diagnostic device for patients with lung diseases.

Prediction of oral squamous cell carcinoma based on machine learning of breath samples: a prospective controlled study

Background

The aim of this study was to evaluate the possibility of breath testing as a method of cancer detection in patients with oral squamous cell carcinoma (OSCC).

Methods

Breath analysis was performed in 35 OSCC patients prior to surgery. In 22 patients, a subsequent breath test was carried out after surgery. Fifty healthy subjects were evaluated in the control group. Breath sampling was standardized regarding location and patient preparation. All analyses were performed using gas chromatography coupled with ion mobility spectrometry and machine learning.

Results

Differences in imaging as well as in pre- and postoperative findings of OSCC patients and healthy participants were observed. Specific volatile organic compound signatures were found in OSCC patients. Samples from patients and healthy individuals could be correctly assigned using machine learning with an average accuracy of 86–90%.

Conclusions

Breath analysis to determine OSCC in patients is promising, and the identification of patterns and the implementation of machine learning require further assessment and optimization. Larger prospective studies are required to use the full potential of machine learning to identify disease signatures in breath volatiles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-021-01862-z.

Breath Analysis by Secondary Electro-Spray Ionization - Mass Spectrometry to Interrogate Biologically Significant Metabolites Non-Invasively

There is an ever-growing interest in metabolomic profiling using noninvasive, real-time techniques that avoid sample manipulation and are painless for the patients. In this context, breath analysis is gaining much attention, and several ionization techniques have been developed to get insights in real-time into metabolic status by analyzing breath through mass spectrometry, such as Proton transfer reaction mass spectrometry (PTR-MS), Selected ion flow tube mass spectrometry (SIFT-MS), and Secondary electrospray ionization mass spectrometry (SESI-MS). SESI-MS is the most recently developed analytical platform displaying particular adequate characteristics for breath analysis, such as the low detection limits, and the detection of low volatility species, which tend to present a higher biological significance. Here, we review the SESI technology development, the different SESI configurations developed, and the standardization procedures described to translate SESI into the clinical environment. Finally, SESI main applications described in the literature with prompt translation into the clinical environment, namely, biomarker discovery or pharmacokinetics and drug monitoring are revised.

The smell of lung disease: a review of the current status of electronic nose technology

There is a need for timely, accurate diagnosis, and personalised management in lung diseases. Exhaled breath reflects inflammatory and metabolic processes in the human body, especially in the lungs. The analysis of exhaled breath using electronic nose (eNose) technology has gained increasing attention in the past years. This technique has great potential to be used in clinical practice as a real-time non-invasive diagnostic tool, and for monitoring disease course and therapeutic effects. To date, multiple eNoses have been developed and evaluated in clinical studies across a wide spectrum of lung diseases, mainly for diagnostic purposes. Heterogeneity in study design, analysis techniques, and differences between eNose devices currently hamper generalization and comparison of study results. Moreover, many pilot studies have been performed, while validation and implementation studies are scarce. These studies are needed before implementation in clinical practice can be realised. This review summarises the technical aspects of available eNose devices and the available evidence for clinical application of eNose technology in different lung diseases. Furthermore, recommendations for future research to pave the way for clinical implementation of eNose technology are provided.

Cheap and easy human breath collection system for trace volatile organic compounds screening using thermal desorption – gas chromatography mass spectrometry

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By analyzing the VOCs presents in our breath, we could identify if some components should not be present in our bodies, or their concentration is higher or lower than normal.

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To collect breath samples for VOC analysis, we looked into the current available methodologies and, due to their high prices, tried to develop our own easy and cheap device. A simple single use Minigrip LDPE plastic bag was used in this work and its efficiency and performance were tested.

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After breath collection, samples were analyzed using Thermal Desorption (TD) system, coupled with Gas Chromatography Mass Spectrometer (GC-MS).

Mass Spectrometry-Based Human Breath Analysis: Towards COVID-19 Diagnosis and Research

COVID-19 is a highly contagious respiratory disease that can be infected through human exhaled breath. Human breath analysis is an attractive strategy for rapid diagnosis of COVID-19 in a non-invasive way by monitoring breath biomarkers. Mass spectrometry (MS)-based approaches offer a promising analytical platform for human breath analysis due to their high speed, specificity, sensitivity, reproducibility, and broad coverage, as well as its versatile coupling methods with different chromatographic separation, and thus can lead to a better understanding of the clinical and biochemical processes of COVID-19. Herein, we try to review the developments and applications of MS-based approaches for multidimensional analysis of COVID-19 breath samples, including metabolites, proteins, microorganisms, and elements. New features of breath sampling and analysis are highlighted. Prospects and challenges on MS-based breath analysis related to COVID-19 diagnosis and study are discussed.

Validation of breath biomarkers for obstructive sleep apnea

BACKGROUND AND OBJECTIVES

Obstructive sleep apnea (OSA) is an underdiagnosed respiratory disease with negative metabolic and cardiovascular effects. The current gold standard for diagnosing OSA is in-hospital polysomnography, a time-consuming and costly procedure, often inconvenient for the patient. Recent studies revealed evidence for the potential of breath analysis for the diagnosis of OSA based on a disease-specific metabolic pattern. However, none of these findings were validated in a larger and broader cohort, an essential step for its application in clinics.

METHODS

In the present study, we validated a panel of breath biomarkers in a cohort of patients with possible OSA (N = 149). These markers were previously identified in our group by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS).

RESULTS

Here, we could confirm significant differences between metabolic patterns in exhaled breath from OSA patients compared to control subjects without OSA as well as the association of breath biomarker levels with disease severity. Our prediction of the diagnosis for the patients from this completely independent validation study using a classification model trained on the data from the previous study resulted in an area under the receiver operating characteristic curve of 0.66, which is comparable to questionnaire-based OSA screenings.

CONCLUSIONS

Thus, our results suggest that breath analysis by SESI-HRMS might be useful to screen for OSA as an objective measure. However, its true predictive power should be tested in combination with OSA screening questionnaires.

CLINICAL TRIAL

"Mass Spectral Fingerprinting in Obstructive Sleep Apnoea", NCT02810158, www.ClinicalTrials.gov.

Calculated indices of volatile organic compounds (VOCs) in exhalation for lung cancer screening and early detection

BACKGROUND

Breath analysis is a promising noninvasive technique that offers a wide range of opportunities to facilitate early diagnosis of lung cancer (LC).

METHOD

Exhaled breath samples of 352 subjects including 160 with lung cancer (LC), 70 with benign pulmonary nodule (BPN) and 122 healthy controls (HC) were analyzed through thermal desorption coupled with gas chromatography-mass spectrometry (TD-GC-MS) to obtain the metabolic information from volatile organic compounds (VOCs). Statistical classification models were used to find diagnostic clusters of VOCs for the discrimination of HC, BPN and LC patients' early and advanced stages, as well as subtypes of LC. Receiver operator characteristics (ROC) curves with 5-fold validations were used to evaluate the accuracy of these models.

RESULTS

The analysis revealed that 20, 19, 19, and 20 VOCs discriminated LC from HC, LC from BPN, histology and LC stages respectively. The calculated diagnostic indices showed a large area under the curve (AUC) to distinguish HC from LC (AUC: 0.987, 95 % confidence interval (CI): 0.976-0.997), BPN from LC (AUC: 0.809, 95 % CI: 0.758-0.860), NSCLC from SCLC (AUC: 0.939, 95 % CI: 0.875-0.995) and Stage III from stage III-IV (AUC: 0.827, 95 % CI: 0.768-0.886). The comparison between the high-risk groups (BPN and HC smokers) and early stages LC resulted in the AUC of 0.756 (95 %CI: 0.681-0.817) for BPN vs. early stage LC and AUC of 0.986 (95 % CI: 0.972-0.994) for HC smoker vs. early stage LC.

CONCLUSION

Volatome of breath of the LC patients was significantly different from that of both BPN patients and HC and showed an ability of distinguishing early from advance stage LC and NSCLC from SCLC. We conclude that the volatome has a potential to help improve early diagnosis of LC.

Metabolomics of exhaled breath in critically ill COVID-19 patients: A pilot study

BACKGROUND

Early diagnosis of coronavirus disease 2019 (COVID-19) is of the utmost importance but remains challenging. The objective of the current study was to characterize exhaled breath from mechanically ventilated adults with COVID-19.

METHODS

In this prospective observational study, we used real-time, online, proton transfer reaction time-of-flight mass spectrometry to perform a metabolomic analysis of expired air from adults undergoing invasive mechanical ventilation in the intensive care unit due to severe COVID-19 or non-COVID-19 acute respiratory distress syndrome (ARDS).

FINDINGS

Between March 25th and June 25th, 2020, we included 40 patients with ARDS, of whom 28 had proven COVID-19. In a multivariate analysis, we identified a characteristic breathprint for COVID-19. We could differentiate between COVID-19 and non-COVID-19 ARDS with accuracy of 93% (sensitivity: 90%, specificity: 94%, area under the receiver operating characteristic curve: 0·94-0·98, after cross-validation). The four most prominent volatile compounds in COVID-19 patients were methylpent-2-enal, 2,4-octadiene 1-chloroheptane, and nonanal.

INTERPRETATION

The real-time, non-invasive detection of methylpent-2-enal, 2,4-octadiene 1-chloroheptane, and nonanal in exhaled breath may identify ARDS patients with COVID-19.

FUNDING

The study was funded by Agence Nationale de la Recherche (SoftwAiR, ANR-18-CE45-0017 and RHU4 RECORDS, Programme d'Investissements d'Avenir, ANR-18-RHUS-0004), Région Île de France (SESAME 2016), and Fondation Foch.

Circadian Metabolomics from Breath

Metabolites like melatonin are essential in determining circadian phase. In the recent years, comprehensive metabolome analyses have unveiled entire panels of small biomolecules fluctuating in a circadian fashion, thus enabling a more precise determination of inner time and understanding of how circadian clock operates at the molecular level. Emerging analytical techniques allowing for the determination of exhaled metabolites in breath show promise to gain further insights noninvasively and in vivo into circadian metabolism.

Effects of modular ion-funnel technology onto analysis of breath VOCs by means of real-time mass spectrometry

Proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) is a powerful tool for real-time monitoring of trace concentrations of volatile organic compounds (VOCs). The sensitivity of PTR-ToF-MS also depends on the ability to effectively focus and transmit ions from the relatively high-pressure drift tube (DT) to the low-pressure mass analyzer. In the present study, a modular ion-funnel (IF) is placed adjacent to the DT of a PTR-ToF-MS instrument to improve the ion-focusing. IF consists of a series of electrodes with gradually decreasing orifice diameters. Radio frequency (RF) voltage and direct current (DC) electric field are then applied to the electrodes to get the ions focused. We investigated the effect of the RF voltage and DC field on the sensitivity of a pattern of VOCs including hydrocarbons, alcohols, aldehydes, ketones, and aromatic compounds. In a proof-of-concept study, the instrument operating both as normal DT (DC-mode) and at optimal IF conditions (RF-mode) was applied for the breath analysis of 21 healthy human subjects. For the range of investigated VOCs, an improvement of one order of magnitude in sensitivity was observed in RF-mode compared with DC-mode. Limits of detection could be improved by a factor of 2–4 in RF-mode compared with DC-mode. Operating the instrument in RF-mode allowed the detection of more compounds in the exhaled air compared with DC-mode. Incorporation of the IF considerably improved the performance of PTR-ToF-MS allowing the real-time monitoring of a larger number of potential breath biomarkers.

Low-Pressure Photoionization in a Dual-Ion Funnel Injector Coupled to an Orbitrap Mass Spectrometer for Direct Analysis of Human Breath and Head-Space Sampled Coffee Roasts

Low-pressure photoionization (LPPI) is a versatile tool for the mass spectrometric detection of (semi-)volatile organic compounds, (s)VOC. Here, a dual-ion funnel MALDI/ESI ion injector was equipped with a direct-inlet LPPI module. A radio-frequency (RF) drive enabled the implementation of three Kr discharge lamps in a novel design optimized for efficient photoionization and undisturbed ion trajectories. Supported by expansion and collisional cooling and, optionally, dopant vapor, primarily intact radical ions and protonated molecules were generated. Molecular identification was supported by the high-resolving power of an Orbitrap mass analyzer. In our proof-of-concept study, exhaled human breath and head-space sampled coffee grounds were characterized with this high-throughput technique. From breath, a few hundred and for the coffee roasts more than thousand distinct (s)VOC features were recorded. Principal component analysis enabled the differentiation of coffee grounds by origin and roasting protocol.

Modification of an atmospheric pressure photoionization source for online analysis of exhaled breath coupled with quadrupole time-of-flight mass spectrometry

Breath analysis is a promising method for metabolomics studies and clinical diagnosis, as it enables the observation of metabolites in a convenient and noninvasive way. In this work, an atmospheric pressure photoionization (APPI) source was modified for online analysis of exhaled breath by coupling with quadrupole time-of-flight mass spectrometry (QTOFMS). Three parameters, namely, the capillary voltage, the sampling flow and the curtain gas flow of the APPI source, were optimized. Five healthy volunteers, three males and two females, were enrolled to test the performance of modified APPI-QTOFMS by analyzing their exhaled breath. A total of 21 compounds were tentatively identified, and four metabolites, namely, dimethyl selenoxide, δ-valerolactam, hydroxymandelic acid and palmitic amide were detected in the exhaled breath for the first time. The result shows that modified APPI-QTOFMS can be used for the online study of exhaled breath. Graphical abstract.

Impact of capsaicin on aroma release: in vitro and in vivo analysis

Capsaicin is the main bioactive compound in chili pepper that leads to the perception of "spiciness". However, the effect of capsaicin on aroma release in the nose remains unexplained. This is the first study designed to measure capsaicin's impact on aroma release during consumption. In vitro studies, using static headspace analysis by atmospheric pressure chemical ionization-mass spectrometry (APCI-MS), showed no impact of capsaicin (5 ppm) on the gas-liquid partitioning equilibria of a range of aroma compounds. However, a significant reduction in aroma release was observed in vivo, during oral melting of a model ice cube system (p < 0.05) included 5 ppm capsaicin. The total release of aroma into the nasal cavity was decreased, such that only 49% of 3-methylbutanal, 60% of 1-octen-3-ol and 83% of linalool was released. This is the first evidence of capsaicin's reduction effect on aroma release during consumption. It was also found that 5 ppm capsaicin increased saliva secretion by 75%, which may have led to the dilution of aroma compounds in the mouth and directly impacted the aroma release into the nasal cavity. The most hydrophilic compound (3-methylbutanal) was affected by capsaicin to a greater extent than the hydrophobic compound (linalool), the solvent effect of the additional saliva may explain this.

A review of breath analysis techniques in head and neck cancer

Cancers of the head and neck region are a severely disabling group of diseases with no method for early detection. Analysis of exhaled breath volatile organic compounds shows promise as biomarkers for early detection and disease monitoring. This article reviews breath analysis in the setting of head and neck cancer, with a practical focus on breath sampling techniques, detection technologies and valid data analysis methods. Title and abstract keyword searches were conducted on PubMed and Embase databases to identify English language studies without a time-period limitation. The main inclusion criteria were human studies comparing head and neck cancer patients to healthy controls using exhaled breath analysis. Multiple breath collection techniques, three major detection technologies and multiple data analysis methods were identified. However, the variability in techniques and lack of methodological standardization does not allow for adequate study replication or data pooling. Twenty-two volatile organic compounds identified in five studies have been reported to discriminate head and neck cancer patients from healthy controls. Breath analysis for detection of head and neck cancer shows promise as a non-invasive detection tool. However, methodological standardization is paramount for future research study design to provide the potential for translating these techniques into routine clinical use.

Palladium embedded in SnO2 enhances the sensitivity of flame-made chemoresistive gas sensors

Palladium is used commonly to enhance the performance of chemoresistive metal-oxide gas sensors. Typically, this enhancement is attributed to the presence of Pd clusters on the surface of their metal-oxide support (i.e. SnO₂). Possible Pd incorporation or embedding into the support rarely has been considered. Here, SnO₂ particles (15 - 21 nm in diameter measured by N₂ adsorption) with different Pd contents (0 - 3 mol%) were prepared by flame spray pyrolysis (FSP). Leaching these particles with HNO₃ and characterization by inductively coupled plasma - optical emission spectrometry (ICP-OES) indicated that only 36 - 60% of Pd have been removed (e.g., from the SnO₂ surface). The rest was embedded within the SnO₂ particles. Annealing prior to leaching decreased by ~30% that Pd surface content. Most interestingly, such SnO₂ particles (with only embedded Pd) show higher sensor response to acetone, ethanol and CO at 350 °C compared to SnO₂ particles containing both surface and embedded Pd (i.e. before leaching). As a result, such sensors can detect acetone with high (> 25) signal-to-noise ratio at levels down to 5 ppb at 50% relative humidity. Graphical abstractFlame-made SnO₂ nanoparticles with embedded and surface Pd (triangles) exhibit lower sensor response to acetone, ethanol and CO than SnO₂ from which the surface Pd had been removed by leaching (circles).

Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable two-dimensional gas chromatography device

Acute respiratory distress syndrome (ARDS) is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity. As a dynamic syndrome with multiple etiologies, its timely diagnosis is difficult as is tracking the course of the syndrome. Therefore, there is a significant need for early, rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS. Here, we report our work on using human breath to differentiate ARDS and non-ARDS causes of respiratory failure. A fully automated portable 2-dimensional gas chromatography device with high peak capacity (> 200 at the resolution of 1), high sensitivity (sub-ppb), and rapid analysis capability (~ 30 min) was designed and made in-house for on-site analysis of patients' breath. A total of 85 breath samples from 48 ARDS patients and controls were collected. Ninety-seven elution peaks were separated and detected in 13 min. An algorithm based on machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) was developed. As compared to the adjudications done by physicians based on the Berlin criteria, our device and algorithm achieved an overall accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value. The high overall accuracy and high positive predicative value suggest that the breath analysis method can accurately diagnose ARDS. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing practice and improving patient outcomes. Graphical abstract.

Metal-organic frameworks for the sorption of acetone and isopropanol in exhaled breath of diabetics prior to quantitation by gas chromatography

A method is described for non-invasive glucose monitoring of diabetics by means of breath analysis. The metal-organic frameworks (MOFs) ZIF-7, UiO-66 and MOF-5 were chosen as sorbents in packed tubes for sampling and preconcentration of acetone and isopropanol which are established diabetes biomarkers. The MOF UiO-66 was found to be the most appropriate sorbent. Following thermal desorption, acetone and isopropanol where quantified by GC. The method has low limits of detection (0.79-0.84 μg·L^-1) and wide linear ranges (5-2000 μg·L^-1). It is assumed that the good performance of UiO-66 as a sorbent results from its large surface area and unique porous structure, and from van der Waals interactions. The relative standard deviation for six replicate cycles of sampling and preconcentration using one 50 mg UiO-66 packed tube ranged between 2.3 and 6.7% for intra-day assays, and from 2.7 to 4.3% for inter-day assays. A tube packed with 50 mg of UiO-66 packed tube can be used in over 120 cycles of adsorption/desorption without significant loss of collection efficiency. The GC method has been applied for the analysis of diabetic breath samples, and the recoveries from spiked samples ranged from 89.1 to 107.6%. Graphical abstract Schematic presentation of metal-organic frameworks as sorbents combined with thermal desorption-gas chromatography for the determination of acetone and isopropanol in exhaled breath of diabetics.

Electronic Noses in Medical Diagnostics

BACKGROUND

Electronic nose technology is being developed in order to analyse complex mixtures of volatiles in a way parallel to biologic olfaction. When applied in the field of medicine, the use of such devices should enable the identification and discrimination between different diseases. In this review, a comprehensive summary of research in medical diagnostics using electronic noses is presented. A special attention has been paid to the application of these devices and sensor technologies, in response to current trends in medicine.

METHODS

Peer-reviewed research literature pertaining to the subject matter was identified based on a search of bibliographic databases. The quality and relevance of retrieved papers was assessed using standard tools. Their content was critically reviewed and certain information contained therein was compiled in tabularized form.

RESULTS

The majority of reviewed studies show promising results, often surpassing the accuracy and sensitivity of established diagnostic methods. However, only a relatively small number of devices have been field tested. The methods used for sample collection and data processing in various studies were listed in a table, together with electronic nose models used in these investigations.

CONCLUSION

Despite the fact that devices equipped with arrays of chemical sensors are not routinely used in everyday medical practice, their prospective use would solve some established issues in medical diagnostics, as well as lead to developments in prophylactics by facilitating a widespread use of non-invasive screening tests.

Intrusion of chlorinated hydrocarbons and their degradation products from contaminated soil. Measurement of indoor air quality and biomonitoring by analysis of end-exhaled air

A historic soil contamination of chlorinated hydrocarbons from a former dry cleaning shop caused intrusion of vapors into a building currently used as bookshop. The aim of this study was to determine the indoor air quality and the uptake of soil contaminants and their degradation products. Samples of indoor air were collected over one week in the warm and one week in the cold season. Pre-shift and post-shift samples of end-exhaled air were collected from two employees. Chlorinated hydrocarbons were analyzed in indoor air and exhaled air samples using thermal desorption gas chromatography mass spectrometry (TD-GC-MS). Tetrachloroethylene (PER), and its degradation products trichloroethylene (TRI), 1,1-dichloroethylene (1,1-DCE), 1,2-cis-dichloroethylene (1,2-cis-DCE), 1,2-trans-dichloroethylene (1,2-trans-DCE), methylene chloride (MC) and vinyl chloride (VC) were determined in ambient air. PER was the prime contaminant with a week average (±sd) of 805.2 ± 598.6 μg/m³ in June 2016 and 1031 ± 499.3 μg/m³ in December 2017. MC, 1,2-cis-DCE and TRI were detected at concentrations below 2.3 μg/m³. 1,1-DCE and VC were not detected. In exhaled air PER, 1,1-DCE, and MC were detected in both June and December, whereas TRI, 1,2-cis-DCE and 1,2-trans-DCE were only detected on one or two days in the cold season. VC was not detected in exhaled air. For PER, the mean concentrations (±sd) in end-exhaled air increased from a five days (Mon-Fri) average pre-shift value of 22.2 ± 8.0 to a post-shift value of 52.6 ± 15.5 ng/L in the male shop owner (p < 0.01) and in the female cashier these values were 26.0 ± 3.6 and 63.6 ± 12.7 ng/L, respectively (p < 0.01). Intrusion of chlorinated soil contaminants resulted in contamination of indoor air above the current accepted indoor air level for PER of 250 μg/m³. For PER in end-exhaled air an accumulation over the workweek was not observed.

Breath analysis by two-dimensional gas chromatography with dual flame ionisation and mass spectrometric detection - Method optimisation and integration within a large-scale clinical study

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New method for the analysis of exhaled breath VOCs by TD-GC × GC-FID/qMS.

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Optimisation of flow modulation and dual detection alongside clinical requirements.

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Addresses key challenges of using GC × GC for large-scale breath metabolomics.

Precision medicine has spurred new innovations in molecular pathology leading to recent advances in the analysis of exhaled breath as a non-invasive diagnostic tool. Volatile organic compounds (VOCs) detected in exhaled breath have the potential to reveal a wealth of chemical and metabolomic information. This study describes the development of a method for the analysis of breath, based on automated thermal desorption (TD) combined with flow modulated comprehensive two-dimensional gas chromatography (GC×GC) with dual flame ionisation and quadrupole mass spectrometric detection (FID and qMS). The constrained optimisation and analytical protocol was designed to meet the practical demands of a large-scale multi-site clinical study, while maintaining analytical rigour to produce high fidelity data. The results demonstrate a comprehensive method optimisation for the collection and analysis of breath VOCs by GC×GC, integral to the standardisation and integration of breath analysis within large clinical studies.

Multimodal chemometric approach for the analysis of human exhaled breath in lung cancer patients by TD-GC × GC-TOFMS

Lung cancer is the deadliest cancer in developed countries. To reduce its mortality rate, it is important to enhance our capability to detect it at earlier stages by developing early diagnostic methods. In that context, the analysis of exhaled breath is an interesting approach because of the simplicity of the medical act and its non-invasiveness. Thermal desorption comprehensive two-dimensional gas chromatography time of flight mass spectrometry (TD-GC × GC-TOFMS) has been used to characterize and compare the volatile content of human breath of lung cancer patients and healthy volunteers. On the sampling side, the contaminations induced by the bags membrane and further environmental migration of VOCs during and after the sampling have also been investigated. Over a realistic period of 6 h, the concentration of contaminants inside the bag can increase from 2 to 3 folds based on simulated breath samples. On the data processing side, Fisher ratio (FR) and random forest (RF) approaches were applied and compared in regards to their ability to reduce the data dimensionality and to extract the significant information. Both approaches allow to efficiently smooth the background signal and extract significant features (27 for FR and 17 for RF). Principal component analysis (PCA) was used to evaluate the clustering capacity of the different models. For both approaches, a separation along PC-1 was obtained with a variance score around 35%. The combined model provides a partial separation with a PC-1 score of 52%. This proof-of-concept study further confirms the potential of breath analysis for cancer detection but also underlines the importance of quality control over the full analytical procedure, including the processing of the data.

Micro-fabricated packed metal gas preconcentrator for enhanced monitoring of ultralow concentration of isoprene

A novel non-silicon-based micro-preconcentration device, as a pretreatment component in a portable gas chromatography system, was developed for the preconcentration one of the trace volatile organic compounds (VOCs) in the exhaled gases, which is one typical biomarker for the chronic liver disease (CLD). The device was designed as an array of manifold-shaped rectangular metal micro-channels with flat dimensions of 16 mm × 12.6 mm and the internal empty volume is 14.4 μL on the copper substrate. Instead of the non-silicon fabrication process, the traditional laser etching technology (LET) was optimized to etch micro-channels, and vacuum diffusion welding (VDW) was applied to form internal channels. The fabricated chip was filled with Carbopack X adsorbent. In the testing, the metal gas preconcentrator (MGP) was installed in a commercial GC (gas chromatography) and nitrogen was used as carrier gas and desorbed gas. With the MPG, up to 352 of concentration factor can be achieved for 10 ppb isoprene. The developed MGP, which has advantages of high strength, low cost, good thermal conductivities, can potentially be used for non-invasive screening of advanced liver fibrosis by monitoring isoprene concentrations in exhaled breath.

Spectroscopic study of breath ethylene via the mouth and nose

The development of new techniques for breath analysis searching for objective biomarkers of oxidative stress showed promise in non-invasive disclosing health information of the well-being of a person. Although numerous biomarkers have been identified so far using breath analysis, very little is known about their origin if they are metabolic or providing from mouth contamination. For the introduction of breath tests into clinical practice, standardization of sample collection needs to be taken into account. Breath analysis has been performed using laser photoacoustic spectroscopy to evaluate exhaled breath by mouth and nose before and after brushing with toothpaste/baking soda in order to identify the important endogenous biomarkers without contaminant sources. As a known biomarker of oxidative stress in the human body, it is important to accurately assess ethylene from exhaled air. Differences in the concentrations of exhaled ethylene are observed after using toothpaste and baking soda. The levels of ethylene are lower for nose breathing compared with mouth breathing. However, the differences are not significant proving that ethylene is generally endogenous but may still exist some contamination, depending of the oral hygiene of each person. These results may lead to a procedure, whereby subjects should be instructed to use toothpaste before each breath test sampling, to avoid the possibility of contamination of endogenous biomarkers.

Positive matrix factorization: A data preprocessing strategy for direct mass spectrometry-based breath analysis

Interest in exhaled breath has grown considerably in recent years, as breath biosampling has shown promise for non-invasive disease diagnosis, therapeutic drug monitoring, and environmental exposure. Real time breath analysis can be accomplished via direct online mass spectrometry (MS)-based methods, which can provide more accurate and detailed data and an enhanced understanding of the temporal evolution of exhaled VOCs in the breath; however, the complicated chemical composition and large raw datasets involved in breath analysis have hindered the discovery of sources contributing to the exhaled VOCs. The positive matrix factorization (PMF) receptor model has been widely used for source apportionment in atmospheric studies. Since the exhaled VOCs contain compounds from various sources, such as alveolar air, mouth air and respiratory dead-space air, PMF may be also helpful for source apportionment of exhaled VOCs in the breath. Thus, this study explores the application of PMF in the pretreatment of direct breath measurement data. The results indicate that (i) endogenous compounds and background contaminants sources can be readily distinguished by PMF in data obtained from replicate measurements of human exhaled breath at single time points (~30 s/measurement), which may benefit both exhalome investigations and the identification of exposure biomarkers; (ii) sources resolved from online measurement data collected over longer periods (1.5 h) can be used to isolate the evolution of exhaled VOCs and investigate processes such as the pharmacokinetics of ketamine and its major metabolites. Therefore, PMF has shown promise for both data processing and subsequent data mining for the ambient MS-based breath analysis.

Change of Exhaled Acetone Concentration Levels in Patients with Acute Decompensated Heart Failure

Exhaled acetone concentration is one of the expected compounds to be a breath biomarker in heart failure. However, it has not been clarified how exhaled acetone concentration changes in clinical course of heart failure.To investigate whether exhaled acetone concentration changes after treatment in acute decompensated heart failure (ADHF).This study included 19 patients with ADHF (ADHF group) and eight patients with stable heart failure (control group). Exhaled acetone was collected from these patients, and the concentration was measured with gas chromatography.The ADHF group had higher heart rates (P = 0.046), higher New York Heart Association class (P < 0.001), higher levels of brain natriuretic peptide (P = 0.026), blood total ketone bodies (P = 0.015), and exhaled acetone concentration (P < 0.001), compared with the control group. In ADHF group, exhaled acetone concentration significantly decreased after treatment (median: 2.40 versus 0.92 ppm, P < 0.001). However, in the control group, exhaled acetone concentration did not significantly change (median: 0.73 versus 0.49 ppm, P = 0.141).In these preliminary findings, exhaled acetone concentration in patients with ADHF drastically decreased by treatment. Serial exhaled acetone measurement might be useful to evaluate the course of ADHF.

Elevated exhaled acetone concentration in stage C heart failure patients with diabetes mellitus

BACKGROUND

Breath acetone is a noninvasive biomarker of heart failure; however, its significance in heart failure patients with diabetes mellitus has yet to be clarified. The objective of this study is to investigate whether exhaled acetone concentration is a noninvasive biomarker in heart failure patients with diabetes mellitus.

METHODS

This study prospectively included 35 diabetic patients with stage C heart failure and 20 diabetic patients with or at risk of heart failure (stage A or B). Exhaled breath was collected after an overnight fast.

RESULTS

The stage C group had significantly higher brain natriuretic peptide levels, larger left ventricular diameter, lower left ventricular ejection fraction, and more frequent use of β-blocker, compared with the stage A or B group. The stage C group had higher exhaled acetone concentrations than the stage A or B group (p = 0.013). Exhaled acetone concentration was correlated with total ketone bodies (r = 0.588, p < 0.001) and brain natriuretic peptide (r = 0.415, p = 0.002).

CONCLUSION

Stage C heart failure patients with diabetes mellitus have elevated exhaled acetone concentrations. Exhaled acetone concentration could be a noninvasive biomarker in heart failure patients with diabetes mellitus.

Exhaled breath condensate methods adapted from human studies using longitudinal metabolomics for predicting early health alterations in dolphins

Monitoring health conditions is essential to detect early asymptomatic stages of a disease. To achieve this, blood, urine and breath samples are commonly used as a routine clinical diagnostic. These samples offer the opportunity to detect specific metabolites related to diseases and provide a better understanding of their development. Although blood samples are commonly used routinely to monitor health, the implementation of a relatively noninvasive technique, such as exhaled breath condensate (EBC) analysis, may further benefit the well-being of both humans and other animals. EBC analysis can be used to track possible physical or biochemical alterations caused by common diseases of the bottlenose dolphin (Tursiops truncatus), such as infections or inflammatory-mediated processes. We have used an untargeted metabolomic method with liquid chromatography-mass spectrometry analysis of EBC samples to determine biomarkers related to disease development. In this study, five dolphins under human care were followed up for 1 year. We collected paired blood, physical examination information, and EBC samples. We then statistically correlated this information to predict specific health alterations. Three dolphins provided promising case study information about biomarkers related to cutaneous infections, respiratory infections, dental disease, or hormonal changes (pregnancy). The use of complementary liquid chromatography platforms, with hydrophilic interaction chromatography and reverse-phased columns, allowed us to detect a wide spectrum of EBC biomarker compounds that could be related to these health alterations. Moreover, these two analytical techniques not only provided complementary metabolite information but in both cases they also provided promising diagnostic information for these health conditions. Graphical abstract Collection of the exhaled condensed breath from a bottlenose dolphin from U.S. Navy Marine Mammal Program (MMP).