The Influence of Smoking Status on Exhaled Breath Profiles in Asthma and COPD Patients

Breath analysis using eNose technology can be used to discriminate between asthma and COPD patients, but it remains unclear whether results are influenced by smoking status. We aim to study whether eNose can discriminate between ever- vs. never-smokers and smoking <24 vs. >24 h before the exhaled breath, and if smoking can be considered a confounder that influences eNose results. We performed a cross-sectional analysis in adults with asthma or chronic obstructive pulmonary disease (COPD), and healthy controls. Ever-smokers were defined as patients with current or past smoking habits. eNose measurements were performed by using the SpiroNose. The principal component (PC) described the eNose signals, and linear discriminant analysis determined if PCs classified ever-smokers vs. never-smokers and smoking <24 vs. >24 h. The area under the receiver–operator characteristic curve (AUC) assessed the accuracy of the models. We selected 593 ever-smokers (167 smoked <24 h before measurement) and 303 never-smokers and measured the exhaled breath profiles of discriminated ever- and never-smokers (AUC: 0.74; 95% CI: 0.66–0.81), and no cigarette consumption <24h (AUC 0.54, 95% CI: 0.43–0.65). In healthy controls, the eNose did not discriminate between ever or never-smokers (AUC 0.54; 95% CI: 0.49–0.60) and recent cigarette consumption (AUC 0.60; 95% CI: 0.50–0.69). The eNose could distinguish between ever and never-smokers in asthma and COPD patients, but not recent smokers. Recent smoking is not a confounding factor of eNose breath profiles.     

Identification of metabolic markers in patients with type 2 Diabetes by Ultrafast gas chromatography coupled to electronic nose. A pilot study

Metabolomics is a potential tool for the discovery of new biomarkers in the early diagnosis of diseases. An ultra-fast gas chromatography system equipped to an electronic nose detector (FGC eNose) was used to identify the metabolomic profile of Volatile Organic Compounds (VOCs) in type 2 diabetes (T2D) urine from Mexican population. A cross-sectional, comparative, and clinical study with translational approach was performed. We recruited twenty T2D patients and twenty-one healthy subjects. Urine samples were taken and analyzed by FGC eNose. Eighty-eight compounds were identified through Kovats's indexes. A natural variation of 30% between the metabolites, expressed by study groups, was observed in Principal Component 1 and 2 with a significant difference (p < 0.001). The model, performed through a Canonical Analysis of Principal coordinated (CAP), allowed a correct classification of 84.6% between healthy and T2D patients, with a 15.4% error. The metabolites 2-propenal, 2-propanol, butane- 2,3-dione and 2-methylpropanal, were increased in patients with T2D, and they were strongly correlated with discrimination between clinically healthy people and T2D patients. This study identified metabolites in urine through FGC eNose that can be used as biomarkers in the identification of T2D patients. However, more studies are needed for its implementation in clinical practice.     

Detection of volatile organic compounds (VOCs) in exhaled breath of patients with chronic obstructive pulmonary disease (COPD) by ion mobility spectrometry

COPD is a disease characterised by a chronic inflammation of the airways and a not fully reversible airway obstruction. The spirometry is considered as gold-standard to diagnose the disease and to grade its severity. In this study we used the methodology of Ion Mobility Spectometry in order to detect Volatile Organic Compounds (VOCs) in exhaled breath of patients with COPD. The purpose of this study was to investigate if the VOCs detected in patients with COPD were different from the VOCs detected in exhaled breath of healthy controls. 13 COPD patients and 33 healthy controls were included in the study. Breath samples were collected via a side-steam Teflon tube and directly measured by an ion mobility spectrometer coupled to a multi capillary column (MCC/IMS). One peak was identified only in the patients group compared to the healthy control group. Consequently, the analysis of exhaled breath could be a useful tool to diagnose COPD.     

Differentiation of chronic obstructive pulmonary disease (COPD) including lung cancer from healthy control group by breath analysis using ion mobility spectrometry

Non-invasive methods with potential for diagnosis of lung diseases gain increasing interest. Within the present study the exhaled breath of 132 persons (97 Chronic obstructive pulmonary disease (COPD) patients [35 COPD without lung cancer, 62 COPD with lung cancer] and 35 healthy volunteers) was investigated using an Ion Mobility Spectrometer (IMS) coupled to a Multi-Capillary Column (MCC) without any pre-separation or pre-enrichment. One hundred four different peaks were considered within the IMS-Chromatograms of the 10 mL breath samples of both groups. A principal component analysis (PCA) of these 104 peaks identified a single analyte, that allowed a separation of the healthy persons and the COPD patients (with and without lung cancer). The sensitivity obtained was 60%, the specificity 91%, the positive predictive value 95%. The peak was characterized as cyclohexanone (CAS 108-94-1). Subsequent studies must validate the identity of the peak used for separation of the two groups with a greater population and external standards. Breath gas analysis using ion mobility spectrometry offers a chance of separating healthy persons and COPD patients using a single analyte at a defined concentration.     

Noninvasive detection of colorectal cancer by analysis of exhaled breath

There has been growing interest in exhaled breath analysis for cancer screening and disease monitoring; however, limited breath biomarker information exists regarding colorectal cancer (CRC). The objective of this study was to screen for breath biomarkers of CRC. Exhaled breath was collected from 20 CRC patients and 20 healthy controls; subsequently, solid-phase microextraction–gas chromatography/mass spectrometry (SPME-GC/MS) was used to assess the exhaled volatile organic compounds (VOCs) of the study participants. The statistical methods of principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed to process the final data. The VOCs in the exhalations of CRC patients exhibited significant differences from the VOCs in the exhalations of healthy controls; in particular, relative to the latter exhalations, the former exhalations contain significantly higher levels of cyclohexanone, 2,2-dimethyldecane, dodecane, 4-ethyl-1-octyn-3-ol, ethylaniline, cyclooctylmethanol, trans-2-dodecen-1-ol, and 3-hydroxy-2,4,4-trimethylpentyl 2-methylpropanoate but significantly lower levels of 6-t-butyl-2,2,9,9-tetramethyl-3,5-decadien-7-yne (P?<?0.05). Analyses of breath VOCs provide a related model of CRC exhalation that could represent an effective and convenient screening method for this disease.

Volatile Organic Compounds Analysis in Breath Air in Healthy Volunteers and Patients Suffering Epidermoid Laryngeal Carcinomas

Exhaled breath contains thousands of gaseous volatile organic compounds (VOCs) that may be used as non-invasive markers of head and neck epidermoid cancer. We hypothesized that solid phase micro-extraction coupled to gas chromatography–mass spectrometry can discriminate patients with epidermoid head and neck cancer from healthy controls by analyzing the gaseous volatile organic compounds, VOC-profile, in exhaled breath, thus identifying some non-invasive biomarkers to be used in early detection. Twenty healthy subjects participated in a cross-sectional study plus 11 patients with epidermoid supraglottic laryngeal cancer. VOCs from T3 supraglottic cancer were clustered distinctly from those of T1 and healthy subjects. Up to seven VOCs were detected differently from healthy volunteers, mainly 2-butanone and ethanol. Thus VOC-patterns of exhaled breath may discriminate patients with epidermoid head and neck cancer from healthy controls.     

Statistical and bioinformatical methods to differentiate chronic obstructive pulmonary disease (COPD) including lung cancer from healthy control by breath analysis using ion mobility spectrometry

Human breath analysis is a powerful and especially a non-invasive technique for the monitoring and hopefully also for the diagnosis of respiratory diseases, including chronic obstructive pulmonary disease (COPD). The exhaled breath of 95 patients suffering COPD and of 35 healthy controls was investigated using an Ion Mobility Spectrometer (IMS) coupled to a Multi-Capillary Column (MCC) without any pre-separation or pre-enrichment. Starting with the results from a Mann–Whitney-Wilcoxon rank sum test to find analytes with the highest potential with respect to differentiation, box and whisker plots, metabolic maps and probability charts were introduced and compared. In addition, the sensitivity, specificity, positive and negative predictive values and the accuracy of the relation were also summarized. The findings were compared to the results of a principal component analysis. Finally, decision trees were introduced to visualize the interdependencies between the analytes and the classifications. The application of these biostatistical methods with simultaneous inclusion of several VOCs for disease classification by ion mobility spectrometry of human breath will provide much more information than using single peaks and single concentration dependencies for disease classification and discrimination of various groups. Towards the future application of potential biomarkers for clinical diagnostic procedures, complex analytical methods, such as ion mobility spectrometry, need statistical and bioinformatical tools which are simple in application, visualize the results and support decisions on the basis of the data obtained from measurements of analytes in exhaled human breath.     

基于气相离子迁移谱研究肺隐球病患者呼出气中特征挥发性有机物

该文采用气相色谱离子迁移谱(GC-IMS)对81例受试者呼出气样品进行了检测,包括临床确诊的20例肺隐球菌病患者,以及28例临床确诊的肺曲霉病患者和33例健康个体的对照样品,单个样品分析仅需10 min。结果显示,所有呼气样品中共检出19种挥发性有机物(VOCs)。通过主成分(PCA)分析发现,肺隐球病患者呼气中VOCs与健康对照组差异显著,但与肺曲霉病患者呼气中VOCs差异不显著。正交偏最小二乘判别模型(OPLS-DA)分析显示,与健康人群相比,肺隐球菌病患者呼气中特征VOCs为2-甲基-1-丙醛、异丙苯、2-戊酮、4-甲基-2-戊酮、丁醛和己醛;肺曲霉病患者的呼气特征VOCs为2-丁酮、2-戊酮、异丙苯、2-甲基-1-丙醛、4-甲基-2-戊酮和3-戊酮。此外,2种肺部感染患者呼气中特征性VOCs均包括2-甲基-1-丙醛、异丙苯、4-甲基-2-戊酮和2-戊酮,这表明丁醛和己醛在肺隐球菌病患者呼气中特异性略强,而2-丁酮和3-戊酮在肺曲霉病患者呼气中特异性更好。综上,采用GC-IMS可快速检出肺隐球病患者呼气中的VOCs,可用于后续数百例甚至数千例大样本分析,为更客观地评价呼气用于肺部真菌感染诊断的可行性提供必要的基础数据。     

Colorimetric Artificial Nose for Identification of Breath Volatile Organic Compounds of Patients with Lung Cancer

A novel and highly sensitive colorimetric sensor array was developed for the detection and identification of breath volatile organic compounds（VOCs） of patients with lung cancer.Employing dimeric metalloporphyrins,metallosalphen complexes,and chemically responsive dyes as the sensing elements,the developed sensor array of artificial nose shows a unique pattern of colorific changes upon its exposure to eight less-reactive VOCs and their mixture gas at a concentration of 735 nmol/L within 3 min.Potential of quantitative analysis of VOCs samples was proved.A good linear relationship of 490-3675 nmol/L was obtained for benzene vapor with a detection limit of 49 nmol/L（S/N=3）.Data analysis was carried out by Hierarchical cluster analysis（HCA） and principal component analysis（PCA）.Each category of breath VOCs clusters together in the PCA score plot.No errors in classification by HCA were observed in 45 trials.Additionaly,the colorimetric sensor array showed good reproducibility under the cyclic sensing experiments.These results demonstrate that the developed colorimetric artificial nose system is an excellent sensing platform for the identification and quantitative analysis of breath VOCs of patients with lung cancer.     

Non-invasive spatial visualization system of exhaled ethanol for real-time analysis of ALDH2 related alcohol metabolism

A novel imaging system of ethanol in exhaled breath induced by acetaldehyde dehydrogenase (ALDH2)-related alcohol metabolism has been developed. The system provides an image of ethanol distribution as chemiluminescence (CL) on an enzyme-immobilized support. The spatiotemporal change of CL generated by ethanol in exhaled breath after oral administration of ethanol was detected by employing an electron multiplier CCD (EM-CCD) camera, illustrated and analyzed. Prior to measurement of standard gaseous ethanol and ethanol in exhaled breath, the system was optimized by investigating the enzyme-immobilized supports, concentration of substrate and pH condition of Tris-HCl buffer solution. The ethanol skin patch test, a simple method as an indicator of ALDH2, was performed on healthy volunteers. Breath samples of 5 volunteers with ALDH2 (+) and 5 volunteers with ALDH2 (-) were used for exhaled ethanol analysis. Concentration-time profiles of exhaled ethanol obtained from all volunteers were analyzed over a period of 120 min after oral administration of ethanol (0.4 g per kg body weight) in the form of beer which contains 5% of alcohol. The results obtained from the system showed that the peaks of exhaled ethanol concentrations appeared at 30 min, which was considered as a rapid ethanol absorption phase following first-order kinetics. Exhaled ethanol concentrations of volunteers with ALDH2 (+) were lower than volunteers with ALDH2 (-) and the digestion of ethanol in volunteers with ALDH2 (+) was faster than in volunteers with ALDH2 (-). The eliminations were analyzed to follow zero-order kinetics with a rate constant for each group.     

Determination of volatile organic compounds in human breath for Helicobacter pylori detection by SPME-GC/MS

Helicobacter pylori living in the human stomach release volatile organic compounds (VOCs) that can be detected in expired air. The aim of the study was the application of breath analysis for bacteria detection. It was accomplished by determination of VOCs characteristic for patients with H. pylori and the analysis of gases released by bacteria in suspension. Solid-phase microextraction was applied as a selective technique for preconcentration and isolation of analytes. Gas chromatography coupled with mass spectrometry was used for the separation and identification of volatile analytes in breath samples and bacterial headspace. For data calculation and processing, discriminant and factor analyses were used. Endogenous substances such as isobutane, 2-butanone and ethyl acetate were detected in the breath of persons with H. pylori in the stomach and in the gaseous mixture released by the bacteria strain but they were not identified in the breath of healthy volunteers. The canonical analysis of discrimination functions showed a strong difference between the three examined groups. Knowledge of substances emitted by H. pylori with the application of an optimized breath analysis method might become a very useful tool for noninvasive detection of this bacterium.     

Comprehensive Two-Dimensional Gas Chromatography–Mass Spectrometry Analysis of Exhaled Breath Compounds after Whole Grain Diets

Exhaled breath is a potential noninvasive matrix to give new information about metabolic effects of diets. In this pilot study, non-targeted analysis of exhaled breath volatile organic compounds (VOCs) was made by comprehensive two-dimensional gas chromatography–mass spectrometry (GCxGC-MS) to explore compounds relating to whole grain (WG) diets. Nine healthy subjects participated in the dietary intervention with parallel crossover design, consisting of two high-fiber diets containing whole grain rye bread (WGR) or whole grain wheat bread (WGW) and 1-week control diets with refined wheat bread (WW) before both diet periods. Large interindividual differences were detected in the VOC composition. About 260 VOCs were detected from exhaled breath samples, in which 40 of the compounds were present in more than half of the samples. Various derivatives of benzoic acid and phenolic compounds, as well as some furanones existed in exhaled breath samples only after the WG diets, making them interesting compounds to study further.     

Chromatographic analysis of VOC patterns in exhaled breath from smokers and nonsmokers

Cigarette smoking harms nearly every organ of the body and causes many diseases. The analysis of exhaled breath for exogenous and endogenous volatile organic compounds (VOCs) can provide fundamental information on active smoking and insight into the health damage that smoke is creating. Various exhaled VOCs have been reported as typical of smoking habit and recent tobacco consumption, but to date, no eligible biomarkers have been identified. Aiming to identify such potential biomarkers, in this pilot study we analyzed the chemical patterns of exhaled breath from 26 volunteers divided into groups of nonsmokers and subgroups of smokers sampled at different periods of withdrawal from smoking. Solid‐phase microextraction technique and gas chromatography/mass spectrometry methods were applied. Many breath VOCs were identified and quantified in very low concentrations (ppbv range), but only a few (toluene, pyridine, pyrrole, benzene, 2‐butanone, 2‐pentanone and 1‐methyldecyclamine) were found to be statistically significant variables by Mann–Whitney test. In our analysis, we did not consider the predictive power of individual VOCs, as well as the criterion of uniqueness for biomarkers suggests, but we used the patterns of the only statistically significant compounds. Probit prediction model based on statistical relevant VOCs‐patterns showed that assessment of smoking status is heavily time dependent. In a two‐class classifier model, it is possible to predict with high specificity and sensitivity if a subject is a smoker who respected 1 hour of abstinence from smoking (short‐term exposure to tobacco) or a smoker (labelled "blank smoker") after a night out of smoking (long‐term exposure to tobacco). On the other side, in our study "blank smokers" are more like non‐smokers so that the two classes cannot be well distinguished and the corresponding prediction results showed a good sensitivity but low selectivity.     

An adaptive breath sampler for use with human subjects with an impaired respiratory function

An adaptive sampler for collecting 2.5 dm(3) samples of exhaled air from human subjects with an impaired respiratory function is described. Pressure in the upper respiratory tract is continuously monitored and the data used to control an automated system to collect select portions of the expired breathing cycle onto a mixed bed Tenax(trade mark) and Carbotrap(trade mark) adsorbent trap for analysis by GC-MS. The sampling approach is intended for use in metabolomic profiling of volatiles in human breath at concentrations greater than microg m(-3). The importance of experimental reproducibility in metabolomic data is emphasised and consequently a high purity air supply is used to maintain a stable exogenous volatile organic compound profile at concentrations in the range 5 to 30 microg m(-3). The results of a 90 day stability study showed that exogenous VOCs were maintained at significantly lower levels (40 times lower for isopropyl alcohol) and with significantly higher reproducibility (80 times lower standard deviation for isopropyl alcohol) than would have been be the case if ambient air had been used. The sampling system was evaluated with healthy controls alongside subjects with chronic obstructive pulmonary disease. Subjects were able to breathe normally with control subjects observed to breathe at a rate of 9 to 17 breaths per minute, compared to 16 to 30 breaths per minute for subjects with COPD. This study presents, for the first time, observations and estimates of intra-subject breath sample reproducibility from human subjects. These reproducibility studies indicated that VOCs in exhaled breath exhibit a variety of dynamic behaviours, with some species recovered with a RSD <30%, while other species were observed to have significantly more variable concentrations, 30 to 130% RSD. The approach was also demonstrated to reliably differentiate the differences in the VOC profiles between alveolar and dead space air.     

Detection of infectious agents in the airways by ion mobility spectrometry of exhaled breath

Diseases of the lung, e. g. chronic obstructive pulmonary disease (COPD), interstitial lung diseases, bronchiectasis or cystic fibrosis, often lead to recurrent severe respiratory infections that cause exacerbations of the underlying disease. These acute or chronic inflammatory processes can result in a progressive destruction of the lung and in an ongoing decline in lung function. Therefore longer inpatient stays for intravenous antibiotic treatment are necessary and the quality of life in these patients is severely limited. A rapid detection of infectious agents in human lungs is often crucial, because the choice of the appropriate therapeutic regime depends at first on the identification of the infecting species. Standard methods for detection and identification are either time consuming, of low sensitivity or expensive. It is known that bacteria, and also mitosporic fungi, produce volatile organic compounds (VOCs) that can be detected in exhaled breath by ion mobility spectrometry (IMS), were a distinct detection of a specific VOC is related to a “peak”. We investigated, whether the detection and characterisation of VOCs by Multi-capillary column coupled to IMS in exhaled breath of patients whose airways are either infected or colonized by Pseudomonas aeruginosa compared to healthy non-smoker controls is capable of identifying those infectious agents. To realize a non invasive identification of pathogens, the exhaled breath of 53 persons (24 patients suffering chronic or infectious on Pseudomonas and 29 healthy controls) was investigated using an ion mobility spectrometer type BioScout. In total 224 different signals were found. Actually, 21 different signals are able to differentiate the two groups, Control and Pseudomonas, with rank sum values less than 0.2. For all 224 signals Box-and-Wisker plots were realized. The peaks with the lowest rank sum values F (0,107) and PS0 (0,112) show rather good separation of both groups. Our preliminary results demonstrate that distinct patterns of a small number of IMS-peaks are sufficient for the identification of these infectious agents. Therefore MCC-IMS seems to be a promising method for the non-invasive identification of patients which are colonized or infected with bacteria such as Pseudomonas aeruginosa.     

A selected ion flow tube mass spectrometry study of ammonia in mouth- and nose-exhaled breath and in the oral cavity

A study has been carried out, involving three healthy volunteers, of the ammonia levels in breath exhaled via the mouth and via the nose and in the static oral cavity using on-line, selected ion flow tube mass spectrometry (SIFT-MS), obviating the problems associated with sample collection of ammonia. The unequivocal conclusion drawn is that the ammonia appearing in the mouth-exhaled breath of the three volunteers is largely generated in the oral cavity and that the ammonia originating at the alveolar interface in the lungs is typically at levels less than about 100 parts-per-billion, which is a small fraction of the total breath ammonia. This leads to the recommendation that exhaled breath analyses should focus on nose-exhaled breath if the objective is to use breath analysis to investigate systemic, metabolic disease.     

人体呼出气中挥发性有机化合物的检测方法

呼出气检测作为一种潜在的新型临床检测手段受到广泛关注。本文详细综述了人体呼出气中挥发性有机化合物(VOCs)的各类检测方法和技术,分别对色谱法、质谱法和光谱及传感器法的原理、特点和最新研究进展进行了介绍,对照总结了目前已确定的异戊二烯、丙酮等疾病生物标志物的各种分析方法和实测数据,并展望了未来的研究动向。     

Breathing Rhythm Variations during Wash-In Do Not Influence Exhaled Volatile Organic Compound Profile Analyzed by an Electronic Nose

E-noses are innovative tools used for exhaled volatile organic compound (VOC) analysis, which have shown their potential in several diseases. Before obtaining a full validation of these instruments in clinical settings, a number of methodological issues still have to be established. We aimed to assess whether variations in breathing rhythm during wash-in with VOC-filtered air before exhaled air collection reflect changes in the exhaled VOC profile when analyzed by an e-nose (Cyranose 320). We enrolled 20 normal subjects and randomly collected their exhaled breath at three different breathing rhythms during wash-in: (a) normal rhythm (respiratory rate (RR) between 12 and 18/min), (b) fast rhythm (RR > 25/min) and (c) slow rhythm (RR < 10/min). Exhaled breath was collected by a previously validated method (Dragonieri et al., J. Bras. Pneumol. 2016) and analyzed by the e-nose. Using principal component analysis (PCA), no significant variations in the exhaled VOC profile were shown among the three breathing rhythms. Subsequent linear discriminant analysis (LDA) confirmed the above findings, with a cross-validated accuracy of 45% (p = ns). We concluded that the exhaled VOC profile, analyzed by an e-nose, is not influenced by variations in breathing rhythm during wash-in.     

Kinetics of ethanol decay in mouth‐ and nose‐exhaled breath measured on‐line by selected ion flow tube mass spectrometry following varying doses of alcohol

A study has been carried out of the decay of ethanol in mouth‐exhaled and nose‐exhaled breath of two healthy volunteers following the ingestion of various doses of alcohol at different dilutions in water. Concurrent analyses of sequential single breath exhalations from the two volunteers were carried out using selected ion flow tube mass spectrometry, SIFT‐MS, on‐line and in real time continuously over some 200 min following each alcohol dose by simply switching sampling between the two volunteers. Thus, the time interval between breath exhalations was only a few minutes, and this results in well‐defined decay curves. Inspection of the mouth‐exhaled and nose‐exhaled breath data shows that mouth contamination of ethanol diminished to insignificant levels after a few minutes. The detailed results of the analyses of nose‐exhaled breath show that the peak levels and the decay rates of breath ethanol are dependent on the ethanol dose and the volume of ethanol/water mixture ingested. From these data, both the efficiency of the first‐pass metabolism of ethanol and the indications of gastric emptying rates at the various doses and ingested volumes have been obtained for the two volunteers. Additionally and simultaneously, acetaldehyde, acetic acid and acetone were measured in each single breath exhalation. Acetaldehyde, the primary product of ethanol metabolism, is seen to track the breath ethanol. Acetic acid, a possible secondary product of this metabolism, was detected in the exhaled breath, but was shown to largely originate in the oral cavity. Breath acetone was seen to increase over the long period of measurement due to the depletion of nutrients. Copyright © 2010 John Wiley & Sons, Ltd.     

Human exhaled air analytics: biomarkers of diseases

Over the last few years, breath analysis for the routine monitoring of metabolic disorders has attracted a considerable amount of scientific interest, especially since breath sampling is a non-invasive technique, totally painless and agreeable to patients. The investigation of human breath samples with various analytical methods has shown a correlation between the concentration patterns of volatile organic compounds (VOCs) and the occurrence of certain diseases. It has been demonstrated that modern analytical instruments allow the determination of many compounds found in human breath both in normal and anomalous concentrations. The composition of exhaled breath in patients with, for example, lung cancer, inflammatory lung disease, hepatic or renal dysfunction and diabetes contains valuable information. Furthermore, the detection and quantification of oxidative stress, and its monitoring during surgery based on composition of exhaled breath, have made considerable progress. This paper gives an overview of the analytical techniques used for sample collection, preconcentration and analysis of human breath composition. The diagnostic potential of different disease-marking substances in human breath for a selection of diseases and the clinical applications of breath analysis are discussed.