BACKGROUND:Molecular breath research using secondary electrospray ionisation high-resolution
mass spectrometry (SESI-HRMS) commonly uses single-use mouthpieces to facilitate sampling
and reduce contamination risk. Many incorporate bacterial/viral filters, but their influence on
untargeted molecular breath profiles is poorly characterised.

Objective: To determine whether commercially available mouthpieces alter SESI-HRMS breath profiles, identify molecular classes affected by filtered mouthpieces, and assess whether ComBat batch correction reduces mouthpiece-related variance while preserving inter-subject information.

Methods: Three healthy volunteers completed seven testing sets across three measurement days. Each set included all seven mouthpiece types, yielding 147 planned breath measurements, of which 143 were valid. Six mouthpieces incorporated filter membranes; one unfiltered mouthpiece served as reference. Breath was analysed in real time using SESI-HRMS. Data were pre-processed, assessed for mouthpiece effects using guided principal component analysis, corrected using ComBat, and mouthpiece-specific features were putatively annotated against the Human Metabolome Database.

Results: Pre-processing yielded 3,718 reproducible breath features. Mouthpiece type produced a strong batch effect (D = 0.9969, p < 0.001), with three clusters: A/B, C/D/E/F, and the unfiltered reference. Mouthpiece type accounted for 42.7% of total feature variance. The unfiltered reference detected 397 features not reproducibly detected with filtered mouthpieces, enriched in low-molecular-weight features putatively assigned to amino-acid-related compounds, short-chain aldehydes, and amines. Filtered mouthpieces contributed mouthpiece-specific features consistent with potential material-related background signals. ComBat reduced mouthpiece-type clustering (D = 0.1407, p = 0.998) while preserving most inter-subject variance.

Conclusions: Mouthpiece type is a major determinant of detected SESI-HRMS breath profiles. Filtered mouthpieces may attenuate selected low-molecular-weight polar features and introduce mouthpiece-specific background signals. Batch correction reduces systematic mouthpiece-related variance but cannot recover features not transmitted by the sampling interface. Consistent mouthpiece use, explicit reporting, and mouthpiece-aware harmonisation are essential for reproducible molecular breath research.
Halitosis is a prevalent yet often underestimated clinical condition with multifactorial etiology, predominantly arising from intra-oral sources such as periodontal disease, tongue coating and xerostomia. Central to its pathogenesis is the microbial degradation of sulfur-containing substrates, which lead to the production of volatile sulfur compounds and other volatile organic compounds responsible for the characteristic odor. These metabolites are produced by proteolytic anaerobes inhabiting the tongue dorsum, periodontal pockets as well as the dental plaque and predominantly exist as structured biofilms. This review examines halitosis through a systems-based lens, conceptualizing the oral cavity as a dynamic bioreactor governed by microbial consortia and their metabolic pathways, mass transport, enzymatic kinetics, as well as growth conditions attributed to microenvironmental factors. Modeling approaches are required to elucidate the intricate dynamics within oral biofilms, including the heterogeneous structural organization and the fundamental transport and metabolic processes occurring at the biofilm-bulk fluid interface and within the biofilm matrix. These interfacial processes, in turn, regulate the flux of volatile compounds into the oral air. Furthermore, associations between halitosis and dental pathologies, including gingivitis, periodontitis, and periodontal abscesses, are explored through microbial dysbiosis. The review also addresses contemporary management strategies including mechanical debridement, antimicrobial agents and probiotics. Understanding the biochemical, microbial and reactor-like behavior of the oral cavity is critical for the effective diagnosis, monitoring and treatment of halitosis in clinical and research settings.
Volatile organic compounds (VOCs) in human breath have been explored as non-invasive
biomarkers for disease, including respiratory infections and cancer, yet very few breath tests have reached clinical validation and regulatory approval. A major barrier is the difficulty of identifying and controlling confounding factors
that affect volatile exhaled breath composition. A critical and overlooked confounder is the
oral microbiome, which produces VOCs that can obscure the trace volatiles originating from
the lower airways. To investigate this, we conducted an intervention study on sixteen healthy
volunteers, using real-time breath analysis, which demonstrates that oral microbiota rapidly
alter exhaled VOC profiles following a low-dose (0.5 g) oral glucose administration. Acetoin
levels respond promptly to glucose, confirming its oral microbial origin. However, pathogenic
bacteria resulting from respiratory infections can also produce acetoin, underscoring the
challenge of distinguishing sources of breath VOCs. Similarly, other volatiles, such as acetic
acid and ethanol, are also influenced by small glucose doses, complicating their use as
biomarkers in non-targeted volatilomic studies. Recognizing the metabolic context of each
volatile is essential to distinguish infection signals from physiological background. Beyond
serving as a cautionary note for exhaled breath research, these results may encourage the oral
health and dentistry communities to adopt breathomics analytical tools for rapid chairside
diagnostics, transforming respiratory confounders into clinical opportunities for dental care.
Halitosis is a multifactorial condition influenced by airway-related factors and breathing patterns. This study aimed to evaluate the relationship between airway dimensions, craniofacial morphology, and breathing type with halitosis in orthodontic patients. In addition, the agreement between the portable breath analyzer and the organoleptic method, as well as the role of self-reported and physiological halitosis, was investigated. This cross-sectional study included 113 systemically and orally healthy orthodontic patients. Lateral cephalometric radiographs were used to assess the sagittal and vertical skeletal patterns and pharyngeal airway dimensions. Halitosis was evaluated using an organoleptic method as the reference standard and a portable breath analysis device for comparison. Oral hygiene habits, breathing type, and perception of halitosis were recorded using a structured questionnaire. Statistical analyses included appropriate parametric and nonparametric tests as well as multivariate logistic regression. Agreement was assessed using Cohen's kappa coefficient. The overall prevalence of halitosis was 8.9% based on organoleptic assessment. Tooth brushing frequency (p= 0.041) and oral dryness (p= 0.004) were significantly associated with halitosis, whereas airway dimensions, skeletal patterns, and breathing types were not (p> 0.05). Physiological halitosis (p= 0.003) and oral dryness (p= 0.047) were significant predictors of self-reported halitosis. The agreement between the self-reported and objective measurements was weak (κ= 0.264;p< 0.001). A statistically significant and substantial level of agreement was observed between the portable device and the organoleptic assessment (κ= 0.698). Airway-related parameters were not identified as significant determinants of halitosis. Within the limitations of this study, halitosis was more closely associated with self-reported oral dryness and tooth-brushing frequency than with airway morphology. Portable breath analyzers may serve as practical adjunctive tools for clinical assessments when used under standardized conditions.
This article reviews the microbiological mechanisms of halitosis, the oral-gut axis, the role of the gut microbiome, related metabolic pathways, and their associations with gastrointestinal diseases, and explores intervention strategies based on microbial regulation. Halitosis is primarily caused by volatile sulfur compounds produced by oral microorganisms, especially gram-negative anaerobic bacteria, and volatile organic compounds in most extraoral etiologies. Studies have found that intestinal microbial dysregulation can affect the composition of oral flora through the oral-gut axis and aggravate bad breath. Gastrointestinal diseases, such as gastroesophageal reflux disease,Helicobacter pyloriinfection, ulcerative colitis and irritable bowel syndrome can directly or indirectly promote the occurrence of bad breath by changing the intestinal microenvironment and microbial metabolic pathways, such as protein spoilage or short-chain fatty acids imbalance, etc. For microbial interventions, traditional Chinese medicine can not only systemically regulate the oral-gut axis balance, but also effectively alleviate bad breath by targeting sterilization and inhibiting the metabolic activity of pathogenic bacteria. This review focuses on elucidating the complex links between halitosis and the gut microbiome, its metabolic pathways, and gastrointestinal diseases, emphasizing the key role of microbial metabolites in pathological mechanisms. It reveals the importance of the oral-gut axis in systemic health and provides a rationale for developing personalized halitosis management strategies based on microbiome regulation.
Human feces are metabolic waste products that emit a wide range of volatile organic compounds, the result of dynamic interactions between gut microbiota and the host physiology. Fecal gas profiles are recognized as potential indicators of an individual's health status; however, most analyses rely on expensive laboratory equipment such as gas chromatography. In contrast, studies utilizing low-cost gas sensors for stool gas analysis remain limited, partly due to the high sensitivity of gas concentration data to environmental variables such as stool weight and ambient conditions. In this study, gas concentration data (ppm) were converted into normalized gas reduction ratio curves (%), referred to here as time curves, so that temporal gas-release patterns could be compared across samples with reduced influence from absolute signal differences. Fifteen stool samples were collected from four individuals and analyzed according to the Bristol stool scale. Time curves corresponding to the same Bristol stool type showed reproducible convergence into representative temporal profiles, where different stool types exhibited distinguishable gas-decay patterns. These findings suggest that time curve analysis may be useful for characterizing stool-type-related temporal gas patterns while reducing the influence of environmental variability in low-cost fecal gas sensing.
Background.Accurate and non-invasive prediction of uterine contractions is essential for optimizing obstetric care and improving perinatal outcomes. We aimed to develop and validate a diagnostic model that combines exhaled volatile organic compounds (VOCs) patterns and oral microbiota profiles to predict contraction status in term pregnancy.Methods. We prospectively enrolled 84 third-trimester pregnant women and analyzed exhaled breath samples by electronic nose (E-nose). A convolutional neural network (CNN) was used to develop a predictive model of uterine contraction status. Subsequently paired saliva samples from a subset of 15 participants underwent 16 S rRNA gene sequencing to explore potential microbial sources of breath VOCs patterns.Results. The CNN-based E-nose model achieved an area under the curve of 0.63 (95% CI: 0.56-0.71) for differentiating non-contractions from irregular contractions, with performance improving to 0.79 (95% CI: 0.72-0.89) for latent-phase contractions and 0.82 (95% CI: 0.77-0.87) during first-stage labor. Concordant with these findings, 16 S rRNA analysis revealed significant enrichment of specific oral taxa in the contraction group, includingStreptococcus sanguinis(P= 0.0016),Lactobacillus(P= 0.0263),Lautropia(P= 0.0453), andLachnospiraceae NK4A136 group(P= 0.0408). Bacterial interaction network analysis revealed enhanced synergistic relationships among microbial taxa.Conclusion.These preliminary findings suggest that E-nose profiling of exhaled breath may be capable of non-invasively predicting uterine contraction status in late pregnancy, pending validation in larger cohorts. The integration of microbial data provides hypothetical biological insights into the breath VOCs changes, suggesting that breath analysis could eventually support personalized obstetric monitoring after further investigation.
Exercise-induced bronchoconstriction (EIB) commonly develops after exercise in cold, dry environments, but the effects of acute exercise in the cold on airway physiology remain incompletely understood. Exhaled particle (PEx) analysis offers a novel, non-invasive approach to assess respiratory tract lining fluid (RTLF) composition and may provide mechanistic insights into airway responses before clinically detectable changes occur. This study investigated the PEx response to moderate-intensity exercise in sub-zero conditions among healthy atopic and non-atopic individuals. Eighteen participants (14 male, 29 ± 6 years) performed two moderate-intensity exercise trials (30 and 90 min duration) in a climate chamber at -15 °C. PEx samples were collected using the PExA® method, before and 30 min after exercise to assess particle mass and count across eight size bins (0.4-5µm) and for subsequent lipidomic analysis. Exercise induced a significant increase in smaller PEx (0.4-0.7μm;p< 0.01). Three lipid species were altered after 30 min exercise, and 11 after 90 min exercise. Phosphatidylethanolamine PE(16:1_18:0) was the only lipid that consistently increased across both exercise durations (30 min:p= 0.023; 90 min:p= 0.044;g= 0.97). Three specific lipid species showed differential exercise responses between atopic and non-atopic participants, including an oxidised phosphatidylcholine species PC(16:0_9:0;O) (p= 0.006,q= 0.59,g= 0.86). These results provide preliminary insight into airway surfactant responses to exercise in a cold climate and contribute to the growing understanding of how the PExA® method can be applied as a non-invasive tool for respiratory health assessment in an acute setting.
Oxidative stress increases with aging and may influence both sleep physiology and gut microbial activity. Molecular hydrogen, produced by intestinal fermentation, acts as an endogenous antioxidant and can be measured noninvasively in exhaled breath. However, age- and sex-related differences in nocturnal changes of breath hydrogen remain unclear. We analyzed breath hydrogen and methane levels in 166 healthy adults aged 20-85 years. Participants self-collected end-alveolar breath samples at home before sleep and immediately after waking. Breath hydrogen and methane levels were determined by gas chromatography. The older group (n= 91) showed significantly lower breath hydrogen levels after waking compared with the non-older group (n= 75), despite no differences before sleep. No significant sex-related differences were observed. The older group also reported more frequent nocturnal awakenings. These findings suggest that age-related changes in hydrogen dynamics may be associated with sleep-related physiological processes. The overnight change in breath hydrogen was significantly greater in the older group compared with the non-older group, indicating a larger overnight reduction in hydrogen levels in older adults. No significant differences in breath methane levels were observed between groups. Age-related alterations in gut microbiota, gastrointestinal motility, and redox balance may contribute to reduced morning hydrogen levels. Breath hydrogen measurement represents a simple, noninvasive biomarker for assessing physiological changes associated with aging and sleep.
Welding processes generate complex aerosols containing fine and ultrafine particulate matter, metal fumes, and gaseous by-products that may pose significant respiratory risks for exposed workers. Exhaled breath condensate (EBC) has emerged as a promising non-invasive matrix for human biomonitoring, offering the opportunity to assess exposure directly at the pulmonary target site and to evaluate early biological responses. This study systematically reviewed the scientific literature available in PubMed, Scopus, and ISI Web of Science to examine the role of EBC in exposure and effect biomonitoring among welders. Sixteen studies met the inclusion criteria. Overall, the evidence indicates that EBC is a suitable matrix for assessing occupational exposure in welding activities. Several studies reported increased concentrations of metals-including Manganese, Nickel, Iron, and Chromium in welders compared to unexposed controls, as well as higher post-shift levels compared with pre-shift samples across the workweek. Associations between cumulative exposure to inhalable dust and metal concentrations in EBC were also observed, even at low exposure levels. EBC has additionally shown potential for detecting early-effect biomarkers, such as indicators of oxidative stress, lipid peroxidation, protein and nucleic acid oxidation, and inflammatory mediators. These findings provide insight into biochemical alterations occurring in the airway lining fluid of welders. Despite encouraging findings, key methodological issues persist. The lack of standardized protocols for EBC collection, storage, and analysis hampers comparability across studies. Indeed, further research should elucidate EBC production and dilution kinetics and clarify metal toxicokinetics to fully establish EBC as a reliable biomonitoring matrix for occupational health research and practice.
Proton transfer reaction mass spectrometry (PTR-MS) has emerged as a transformative tool in breath analysis, enabling real-time, high-sensitivity profiling of volatile organic compounds down to the pptv level without sample preparation. This review critically examines the technological evolution of PTR-MS, from fundamental ion-molecule kinetics to advanced configurations, including time-of-flight analyzers and switchable reagent ion technologies. We systematically evaluate the clinical potential of PTR-MS for identifying volatile signatures associated with pulmonary malignancies, infectious diseases, and systemic metabolic disorders. Despite these advances, the transition of PTR-MS from exploratory studies to routine clinical application remains limited by three major challenges: insufficient metrological traceability in quantification, qualitative ambiguity arising from isomeric overlap, and poor inter-study comparability caused by non-standardized sampling workflows. To bridge this trust gap, we propose a validation framework centered on traceable calibration, standardized breath sampling, and confidence-ranked qualitative confirmation. In particular, we advocate the use of matrix-matched reference materials for absolute quantification and the development of a standardized PTR-MS spectral atlas integrating GC retention information and reagent-ion-dependent fragmentation behavior. Such a metrological and harmonization-oriented strategy is essential for improving reproducibility, cross-platform comparability, and the clinical translation of PTR-MS in precision medicine.
Early detection is critical for lung cancer patients. One lung cancer detection method under study is using sniffer dogs. This study aimed to evaluate, retrospectively, the sensitivity and specificity of theCancerDetectionDogCollective (CDDC®) method under training conditions. A team of five trained sniffer dogs analyzed breath samples from lung cancer patients and cancer-free volunteers, and a cancer sample is positive if at least three dogs indicate it. Dog handlers and experimental observers were blinded to sample identity, and detection accuracy was assessed. Primary endpoint was sensitivity, and specificity and confounding factors were also assessed. Samples were collected in 2024 from 824 volunteers, including 111 with a confirmed diagnosis of lung cancer (mean age 60, range 34-80, 18% early-stage cancer, 46% not yet oncological treated). A total of 11 900 breath samples were tested with 125 test runs per dog. Each of the five dogs demonstrated a detection performance with a sensitivity between 82% and 89%, a specificity over 95%, and an accuracy over 94%. The CDDC® dog team's corporate decision revealed a sensitivity of at least 95.5%. The cancer-free volunteers were primarily young, healthy individuals. According to the CDDC® decision rules, none of these control samples were identified as false positives by more than two dogs. Analysis of potential confounding factors revealed that weather conditions and supervisor skills were associated with the dogs' performance. The CDDC® method showed high consistency in training scenarios. Further studies should evaluate this method in a controlled clinical study alongside lung cancer screening.
Ischemic heart disease (IHD) remains the leading global cause of mortality and morbidity. Current diagnostic approaches face significant limitations in accessibility, cost, invasiveness, and accuracy, creating an urgent need for innovative non-invasive screening methods. To evaluate the ability of a machine learning (ML) model based on dynamic exhaled breath volatile organic compound (VOC) patterns to detect stress-induced myocardial perfusion defects, used here as the study reference outcome related to IHD. This prospective single-center study enrolled 80 participants (31 with stress-induced myocardial perfusion defects confirmed by multidetector computed tomography with perfusion assessment and 49 controls). All participants underwent real-time breath analysis using PTR-TOF-MS-1000 at rest and after bicycle ergometry stress testing. ML models were developed using delta changes in VOC patterns between baseline and post-exertion measurements, with rigorous leave-one-out cross-validation and comprehensive performance metrics. The weighted ensemble classifier achieved an AUC of 0.743 (95% CI: 0.622-0.840), with a sensitivity of 0.774 (95% CI: 0.600-0.905) and a specificity of 0.633 (95% CI: 0.500-0.760). Feature importance analysis identified specific VOCs, particularly those with m/z 94.053731, 90.951122, and 60.055305, which demonstrated consistent diagnostic significance across different temporal measurement points (delta10). This study demonstrates the feasibility of dynamic exhaled breath analysis combined with ML for the detection of IHD. However, the present findings should be considered preliminary and exploratory, as the model was validated internally only. Given the current sensitivity, the approach is not suitable as a standalone screening tool, but may serve as a non-invasive adjunctive or triage-support tool. External validation in independent cohorts is required before clinical implementation.
Cannabinoids can be captured from breath after cannabis use, but sample processing varies between studies, even when using the same sampling device. The BreathExplor impaction filter device has been used to capture Δ9-tetrahydrocannabinol (THC) in breath after cannabis use in multiple studies, but processing differed in solvent type, keeper addition, and concentration method. In this study we test the effect that container material, vacuum concentration, keeper, elution solvent, cannabinoid mass, agitation, and elution time have on the recovery of THC, cannabidiol, and cannabinol from spiked breath samples. Only container material and keeper had a significant impact on recovery, although limiting the use of plastic vials, one of the two container materials studied, did have some mitigating effects. Despite these two factors having a significant effect, the recovery of cannabinoids remained relatively low. Further investigation showed that our process for the preparation of spiked breath samples, specifically the complete evaporation of a small volume of spike solvent, leads to large cannabinoid losses. As recently published high-accuracy vapor pressure data indicates that these cannabinoids should be considered semi-volatile, a microelution process was additionally explored as it requires less solvent and therefore no concentration is necessary during sample processing. The microelution process resulted in similar or higher recoveries of cannabinoids as compared to the large volume elution process, suggesting that it is a superior processing method in terms of recovery and processing time.
Lung inflammation is associated with a response to pollution or irritants and is reported to release volatile organic compounds (VOCs) in breath, that can be monitored non-invasively. An approach to studying short-term lung inflammation is exposure to environmental emissions. This study used brief exposure to PM₂.₅ emissions from two types of burning candles to evaluate potential differences in airway inflammation among mildly asthmatic individuals. The aim was to explore VOCs linked to lung inflammation by exposure to candle emissions; emissions from candle A, a newly developed candle with a smaller emission to the surroundings and candle B, a well-characterised commercially available candle. Seventeen non-smoking asthmatics (11 female) with a mean age of 21.9 years participated in a randomised controlled double-blind crossover study including two exposure sessions: i) air with candle A emissions (mean PM2.554.1µg m-3), and ii) air with candle B emissions (mean PM2.598.4µg m-3). Participants underwent two five-hour, double-blind exposure sessions in a controlled climate chamber, each involving emissions from one of two candle types. Exhaled breath was collected at baseline (0 h), immediately post-exposure (5 h), and the following morning (24 h). VOCs were analysed using both a targeted panel of 23 predefined compounds and an untargeted workflow, which yielded 21 breath-derived VOCs after ambient-blank filtering. Statistical comparisons used Wilcoxon signed-rank tests with Benjamini-Hochberg correction. Across both exposure sessions, ten exhaled VOCs showed significant changes between baseline and immediate post-exposure (5 h), including increases in several aldehydes and decreases in selected sulfur-containing compounds. These changes were not present at 24 h, where samples resembled baseline profiles. The ten VOCs were identified from a combined dataset of 23 targeted compounds and 21 untargeted breath-derived VOCs; only the untargeted analysis yielded significant findings after correction. PCA demonstrated clear separation between baseline and 5 h samples, particularly for candle B, for which 0 h, 5 h and 24 h samples formed distinct clusters. This candle-specific response produced a multi-VOC signature capable of distinguishing individuals immediately after exposure, suggesting that exhaled VOCs can reflect short-term airway inflammatory responses. The findings suggest that an emission-specific, exhaled breath, multi-VOC profile can detect individuals exposed to inflammation triggers, such as candles. This indicates that VOCs could serve as biomarkers for detecting short-term airway inflammation, as evidenced by the systematic difference in VOCs observed 5 h after exposure to candle B. This research contributes to the advancing field of VOC-based health monitoring and might affect public health implications.
Exhaled breath is a noninvasive and repeatable biological matrix offering new opportunities for respiratory microbiome analysis, yet its extremely low microbial biomass limits current high-throughput applications. Building on our previously developed phase-change drywall cyclone sampler (PDC-sampler), which integrates condensational growth with dry-wall cyclone separation, we established a validated workflow for efficient aerosol collection and multi-Omics sequencing of exhaled breath. Using this platform, exhaled breath from 15 febrile patients and 6 healthy volunteers was analyzed via shotgun metagenomic and 16 S rRNA sequencing to assess microbial composition, diversity, and functional features. The PDC-sampler significantly increased microbial DNA yield, enabling stable detection of bacterial taxa dominated byPseudomonadota, Bacillota, Bacteroidota, andActinomycetota. Functional annotations and diversity metrics revealed distinct microbial and metabolic patterns between individuals, confirming the platform's analytical sensitivity and biological representativeness. This work experimentally validates the feasibility of exhaled breath microbiome sequencing using the PDC-sampler, providing a practical and generalizable framework for noninvasive respiratory microecology studies and future diagnostic applications.
Exhaled breath analysis presents a promising approach for drug monitoring. While the range of drugs known to undergo pulmonary exhalation remains limited, innovative experimental models are needed to explore this field. This study aimed to develop an ex-vivo platform as a general experimental setup for studying volatile and semi-volatile compounds in exhaled breath, using propofol as a pragmatic validation compound because it can be reliably detected both in exhaled air and in blood under our experimental conditions. A porcine lung model was created using lungs from a commercial slaughter. Each experiment was performed with a single isolated lung in an individual setup. The lungs were perfused and ventilated. Propofol exhalation was validated under various conditions (boluses, infusion rates, blood flow, and ventilation) using a propofol calibrated multi-capillary column-ion mobility spectrometer (MCC-IMS). Blood gas analysis and plasma concentration samples were collected every 20 min to continuously monitor propofol plasma levels, pulmonary respiration, and metabolism. We established a functional ex-vivo platform using nine commercially slaughtered porcine lungs, enabling extended measurements for up to 13 h. Hematocrit was set to 35% and hemoglobin to approximately 12 g dl-1, with glucose supplementation of 921 mg h-1. Lactate increased by 308% over the perfusion period. The lungs were ventilated in volume-controlled mode (Vt 700 ml, RR 14 min-1, PEEP 8 mbar, I:E 1:1) and perfused at a standard blood flow of 1.0 l min-1, and mean pH was 7.31 over the perfusion period. Exhaled Propofol was detected on average 19 min after the first administration using MCC-IMS. Changes in blood flow and minute volume were accompanied by corresponding changes in the time course and magnitude of exhaled propofol. This ex-vivo model of perfused and ventilated porcine lungs provides a controlled setting to study the appearance of intravenously administered drugs in exhaled air under defined ventilation and perfusion conditions. The platform enabled prolonged measurements and detection of exhaled propofol signals and may support future screening of candidate drugs for breath-based drug monitoring pending validation across additional compounds and conditions.
Exhaled breath analysis represents a promising non-invasive approach for disease monitoring through volatile organic compounds (VOCs) detection. However, the lack of standardized sampling methods do not enable direct clinical translation. This study compared three widely used offline breath sampling techniques (Tedlar® bags, BioVOC-2®, and ReCIVA®) using the established peppermint benchmarking protocol and comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC × GC-MS). Seven healthy participants completed the peppermint experiment, with breath samples collected at multiple time points following capsule ingestion. Washout curves for targeted terpenoid compounds were analyzed to assess analytical performance, reproducibility, and background contamination across devices. Clinical feasibility was evaluated through focus groups with clinicians, researchers, and study participants. Tedlar® bags demonstrated reliable performance with lowest overall pooled relative standard deviations, though sensitive to exogenous contamination. ReCIVA® showed higher overall variability, superior selectivity and reduced background interference compared to Tedlar® bags (p< 0.01). However, ReCIVA® showed higher complexity, cost, reduced comfort and potential for saliva contamination during extended sampling. BioVOC-2® offered operational simplicity but was limited by small sampling volume (129 ml) reducing its sensitivity and manual handling variability. No single device emerged as universally optimal. Tedlar® bags, when accompanied by rigorous standard operating procedures, remain most suitable for large-scale studies, BioVOC-2® for rapid targeted screening, and ReCIVA® for controlled research requiring high selectivity. Successful clinical implementation will require balancing analytical performance with practical considerations including patient comfort, cost-effectiveness, and workflow integration. These findings support ongoing standardization efforts within the breathomics community and extend peppermint database for exhaled breath sampling.
Exhaled breath analysis presents a promising approach for drug monitoring. While the range of drugs known to undergo pulmonary exhalation remains limited, innovative experimental models are needed to explore this field. This study aimed to develop anex-vivoplatform as a general experimental setup for studying volatile and semi-volatile compounds in exhaled breath, using propofol as a pragmatic validation compound because it can be reliably detected both in exhaled air and in blood under our experimental conditions. A porcine lung model was created using lungs from a commercial slaughter. Each experiment was performed with a single isolated lung in an individual setup. The lungs were perfused and ventilated. Propofol exhalation was validated under various conditions (boluses, infusion rates, blood flow, and ventilation) using a propofol calibrated multi-capillary column-ion mobility spectrometer (MCC-IMS). Blood gas analysis and plasma concentration samples were collected every 20 min to continuously monitor propofol plasma levels, pulmonary respiration, and metabolism. We established a functionalex-vivoplatform using nine commercially slaughtered porcine lungs, enabling extended measurements for up to 13 h. Hematocrit was set to 35% and hemoglobin to approximately 12 g dl-1, with glucose supplementation of 921 mg h-1. Lactate increased by 308% over the perfusion period. The lungs were ventilated in volume-controlled mode (Vt700 ml, RR 14 min-1, PEEP 8 mbar, I:E 1:1) and perfused at a standard blood flow of 1.0 l min-1, and mean pH was 7.31 over the perfusion period. Exhaled Propofol was detected on average 19 min after the first administration using MCC-IMS. Changes in blood flow and minute volume were accompanied by corresponding changes in the time course and magnitude of exhaled propofol. Thisex-vivomodel of perfused and ventilated porcine lungs provides a controlled setting to study the appearance of intravenously administered drugs in exhaled air under defined ventilation and perfusion conditions. The platform enabled prolonged measurements and detection of exhaled propofol signals and may support future screening of candidate drugs for breath-based drug monitoring pending validation across additional compounds and conditions.
This review addresses several important confounding factors that are often overlooked in the analysis of volatiles contained in exhaled breath, which, if ignored, will significantly limit the interpretation of volatile data from exhaled breath and thus prevent meaningful outcomes. Crucial confounding factors that tend to be neglected are those that influence the alveolar volatile concentrations according to the Farhi equation, namely cardiac output, alveolar ventilation, blood:air partition coefficients and mixed-venous blood volatile concentrations. Another potential confounding factor is associated with the contributions of volatiles produced in the oral cavity through microbial activity. In addition, the concentration of an exhaled breath volatile will be affected if that volatile is also present in the ambient inhaled air. The purpose of this review is to show how these confounding factors can be accounted for. We will demonstrate how mathematical modeling and an understanding of the Farhi equation aid in the interpretation of the exhaled breath volatile concentration measurements. We will discuss the limitations of the alveolar gradient method used to determine the effects of inhaled volatiles. An alternative method is presented that correctly allows for any inhaled volatile contribution to the exhaled concentration of that volatile. The review concludes with suggested recommendations that, if adopted, will improve the quality of breath data leading to an improved interpretation of exhaled volatiles.
