Ion mobility spectrometry for the detection of volatile organic compounds in exhaled breath of patients with lung cancer: results of a pilot study

Combined volatolomics for monitoring of human body chemistry

Analysis of volatile organic compounds (VOCs) is a promising approach for non-invasive, fast and potentially inexpensive diagnostics. Here, we present a new methodology for profiling the body chemistry by using the volatile fraction of molecules in various body fluids. Using mass spectrometry and cross-reactive nanomaterial-based sensors array, we demonstrate that simultaneous VOC detection from breath and skin would provide complementary, non-correlated information of the body's volatile metabolites profile. Eventually with further wide population validation studies, such a methodology could provide more accurate monitoring of pathological changes compared to the information provided by a single body fluid. The qualitative and quantitative methods presented here offers a variety of options for novel mapping of the metabolic properties of complex organisms, including humans.

Strengths, weaknesses, and opportunities of diagnostic breathomics in pleural mesothelioma-a hypothesis

Past and present asbestos use will reflect in increasing numbers of mesothelioma cases in the next decades, diagnosed at a late stage and with a dismal prognosis. This stresses the need for early detection tools, which could improve patients' survival. Recently, breath analysis as a noninvasive and fast diagnostic tool has found its way into biomedical research. High-throughput breathomics uses spectrometric, chromatographic, and sensor techniques to diagnose asbestos-related pulmonary diseases based upon volatile organic compounds (VOC) in breath. This article reviews the state-of-the-art available breath analyzing techniques and provides the insight in the current use of VOCs as early diagnostic or prognostic biomarkers of mesothelioma to stimulate further research in this field. Cancer Epidemiol Biomarkers Prev; 23(6); 898-908. ©2014 AACR.

Evaluation and application of static headspace-multicapillary column-gas chromatography-ion mobility spectrometry for complex sample analysis

An evaluation of static headspace-multicapillary column-gas chromatography-ion mobility spectrometry (SHS-MCC-GC-IMS) has been undertaken to assess its applicability for the determination of 32 volatile compounds (VCs). The key experimental variables of sample incubation time and temperature have been evaluated alongside the MCC-GC variables of column polarity, syringe temperature, injection temperature, injection volume, column temperature and carrier gas flow rate coupled with the IMS variables of temperature and drift gas flow rate. This evaluation resulted in six sets of experimental variables being required to separate the 32 VCs. The optimum experimental variables for SHS-MCC-GC-IMS, the retention time and drift time operating parameters were determined; to normalise the operating parameters, the relative drift time and normalised reduced ion mobility for each VC were determined. In addition, a full theoretical explanation is provided on the formation of the monomer, dimer and trimer of a VC. The optimum operating condition for each VC calibration data was obtained alongside limit of detection (LOD) and limit of quantitation (LOQ) values. Typical detection limits ranged from 0.1ng bis(methylthio)methane, ethylbutanoate and (E)-2-nonenal to 472ng isovaleric acid with correlation coefficient (R(2)) data ranging from 0.9793 (for the dimer of octanal) through to 0.9990 (for isobutyric acid). Finally, the developed protocols were applied to the analysis of malodour in sock samples. Initial work involved spiking an inert matrix and sock samples with appropriate concentrations of eight VCs. The average recovery from the inert matrix was 101±18% (n=8), while recoveries from the sock samples were lower, that is, 54±30% (n=8) for sock type 1 and 78±24% (n=6) for sock type 2. Finally, SHS-MCC-GC-IMS was applied to sock malodour in a field trial based on 11 volunteers (mixed gender) over a 3-week period. By applying the SHS-MCC-GC-IMS database, four VCs were identified and quantified: ammonia, dimethyl disulphide, dimethyl trisulphide and butyric acid. A link was identified between the presence of high ammonia and dimethyl disulphide concentrations and a high malodour odour grading, that is, ≥ 6. Statistical analysis did not find any correlation between the occurrence of dimethyl disulphide and participant gender.

Breath analysis in real time by mass spectrometry in chronic obstructive pulmonary disease

BACKGROUND

It has been suggested that exhaled breath contains relevant information on health status.

OBJECTIVES

We hypothesized that a novel mass spectrometry (MS) technique to analyze breath in real time could be useful to differentiate breathprints from chronic obstructive pulmonary disease (COPD) patients and controls (smokers and nonsmokers).

METHODS

We studied 61 participants including 25 COPD patients [Global Initiative for Obstructive Lung Disease (GOLD) stages I-IV], 25 nonsmoking controls and 11 smoking controls. We analyzed their breath by MS in real time. Raw mass spectra were then processed and statistically analyzed.

RESULTS

A panel of discriminating mass-spectral features was identified for COPD (all stages; n = 25) versus healthy nonsmokers (n = 25), COPD (all stages; n = 25) versus healthy smokers (n = 11) and mild COPD (GOLD stages I/II; n = 13) versus severe COPD (GOLD stages III/IV; n = 12). A blind classification (i.e. leave-one-out cross validation) resulted in 96% sensitivity and 72.7% specificity (COPD vs. smoking controls), 88% sensitivity and 92% specificity (COPD vs. nonsmoking controls) and 92.3% sensitivity and 83.3% specificity (GOLD I/II vs. GOLD III/IV). Acetone and indole were identified as two of the discriminating exhaled molecules.

CONCLUSIONS

We conclude that real-time MS may be a useful technique to analyze and characterize the metabolome of exhaled breath. The acquisition of breathprints in a rapid manner may be valuable to support COPD diagnosis and to gain insight into the disease.

A modular computational framework for automated peak extraction from ion mobility spectra

BACKGROUND

An ion mobility (IM) spectrometer coupled with a multi-capillary column (MCC) measures volatile organic compounds (VOCs) in the air or in exhaled breath. This technique is utilized in several biotechnological and medical applications. Each peak in an MCC/IM measurement represents a certain compound, which may be known or unknown. For clustering and classification of measurements, the raw data matrix must be reduced to a set of peaks. Each peak is described by its coordinates (retention time in the MCC and reduced inverse ion mobility) and shape (signal intensity, further shape parameters). This fundamental step is referred to as peak extraction. It is the basis for identifying discriminating peaks, and hence putative biomarkers, between two classes of measurements, such as a healthy control group and a group of patients with a confirmed disease. Current state-of-the-art peak extraction methods require human interaction, such as hand-picking approximate peak locations, assisted by a visualization of the data matrix. In a high-throughput context, however, it is preferable to have robust methods for fully automated peak extraction.

RESULTS

We introduce PEAX, a modular framework for automated peak extraction. The framework consists of several steps in a pipeline architecture. Each step performs a specific sub-task and can be instantiated by different methods implemented as modules. We provide open-source software for the framework and several modules for each step. Additionally, an interface that allows easy extension by a new module is provided. Combining the modules in all reasonable ways leads to a large number of peak extraction methods. We evaluate all combinations using intrinsic error measures and by comparing the resulting peak sets with an expert-picked one.

CONCLUSIONS

Our software PEAX is able to automatically extract peaks from MCC/IM measurements within a few seconds. The automatically obtained results keep up with the results provided by current state-of-the-art peak extraction methods. This opens a high-throughput context for the MCC/IM application field. Our software is available at http://www.rahmannlab.de/research/ims.

Volatile organic compounds and the potential for a lung cancer breath test

SUMMARY Detecting exhaled volatile organic compounds (VOCs) that are associated with lung cancer (LC) has realistic potential for becoming an integral part of population-based LC screening and monitoring in the near future. Here, we review the main three approaches for profiling VOCs in LC patients and their advantages and pitfalls: first, mass spectrometry techniques for the identification and/or quantification of a wide variety of separate breath VOCs; second, canines that are trained to sniff out LC; and third, cross-reactive chemical sensors in combination with statistical methods for identifying disease-specific patterns. We estimate that the latter would be most suitable for clinical practice. In the short run, breath testing could provide a critically needed adjunct method for detecting nodule malignancy with high specificity during low-dose computed tomography screening. In the long run, breath testing holds potential for entirely revolutionizing LC screening, diagnosis and management.

On-site airborne pheromone sensing

Pheromones and other semiochemicals play an important role in the natural world by influencing the behavior of plants, mammals, and insects. In the latter case, species-dependent pheromone communication has numerous applications, including the detection, trapping, monitoring and guiding of insects, as well as pest management in agriculture. On-site sensors are desirable when volatile organic compounds (VOCs) are used as semiochemicals. Insects have evolved highly selective sensors for such compounds, so biosensors comprising complete insects, isolated organs or individual proteins can be highly effective. However, isolated insect organs have a limited lifetime as biosensor, so biomimetic approaches are needed for prolonged monitoring, novel applications, or measurements in challenging environments. We discuss the development of on-site biosensors and biomimetic approaches for airborne-pheromone sensing, together with biomimetic VOC sensor systems. Furthermore, the infochemical effect describing the anthropogenic contamination of the ecosystem through semiochemicals, will be considered in the context of novel on-site pheromone sensing-systems.

Standardised exhaled breath collection for the measurement of exhaled volatile organic compounds by proton transfer reaction mass spectrometry

Background

Exhaled breath volatile organic compound (VOC) analysis for airway disease monitoring is promising. However, contrary to nitric oxide the method for exhaled breath collection has not yet been standardized and the effects of expiratory flow and breath-hold have not been sufficiently studied. These manoeuvres may also reveal the origin of exhaled compounds.

Methods

15 healthy volunteers (34 ± 7 years) participated in the study. Subjects inhaled through their nose and exhaled immediately at two different flows (5 L/min and 10 L/min) into methylated polyethylene bags. In addition, the effect of a 20 s breath-hold following inhalation to total lung capacity was studied. The samples were analyzed for ethanol and acetone levels immediately using proton-transfer-reaction mass-spectrometer (PTR-MS, Logan Research, UK).

Results

Ethanol levels were negatively affected by expiratory flow rate (232.70 ± 33.50 ppb vs. 202.30 ± 27.28 ppb at 5 L/min and 10 L/min, respectively, p < 0.05), but remained unchanged following the breath hold (242.50 ± 34.53 vs. 237.90 ± 35.86 ppb, without and with breath hold, respectively, p = 0.11). On the contrary, acetone levels were increased following breath hold (1.50 ± 0.18 ppm) compared to the baseline levels (1.38 ± 0.15 ppm), but were not affected by expiratory flow (1.40 ± 0.14 ppm vs. 1.49 ± 0.14 ppm, 5 L/min vs. 10 L/min, respectively, p = 0.14). The diet had no significant effects on the gasses levels which showed good inter and intra session reproducibility.

Conclusions

Exhalation parameters such as expiratory flow and breath-hold may affect VOC levels significantly; therefore standardisation of exhaled VOC measurements is mandatory. Our preliminary results suggest a different origin in the respiratory tract for these two gasses.

A mobile instrumentation platform to distinguish airway disorders

Asthma and chronic obstructive pulmonary disease (COPD) are distinct but clinically overlapping airway disorders which often create diagnostic and therapeutic dilemmas. Current strategies to discriminate these diseases are limited by insensitivity and poor performance due to biologic variability. We tested the hypothesis that a gas chromatograph/differential mobility spectrometer (GC/DMS) sensor could distinguish between clinically well-defined groups with airway disorders based on the volatile organic compounds (VOCs) obtained from exhaled breath. After comparing VOC profiles obtained from 13 asthma, 5 COPD and 13 healthy control subjects, we found that VOC profiles distinguished asthma from healthy controls and also a subgroup of asthmatics taking the drug omalizumab from healthy controls. The VOC profiles could not distinguish between COPD and any of the other groups. Our results show a potential application of the GC/DMS for non-invasive and bedside diagnostics of asthma and asthma therapy monitoring. Future studies will focus on larger sample sizes and patient cohorts.

Exhaled volatile organic compounds identify patients with colorectal cancer

BACKGROUND

An effective screening tool for colorectal cancer is still lacking. Analysis of the volatile organic compounds (VOCs) linked to cancer is a new frontier in cancer screening, as tumour growth involves several metabolic changes leading to the production of specific compounds that can be detected in exhaled breath. This study investigated whether patients with colorectal cancer have a specific VOC pattern compared with the healthy population.

METHODS

Exhaled breath was collected in an inert bag (Tedlar(®) ) from patients with colorectal cancer and healthy controls (negative at colonoscopy), and processed offline by thermal-desorber gas chromatography-mass spectrometry to evaluate the VOC profile. During the trial phase VOCs of interest were identified and selected, and VOC patterns able to discriminate patients from controls were set up; in the validation phase their discriminant performance was tested on blinded samples. A probabilistic neural network (PNN) validated by the leave-one-out method was used to identify the pattern of VOCs that better discriminated between the two groups.

RESULTS

Some 37 patients and 41 controls were included in the trial phase. Application of a PNN to a pattern of 15 compounds showed a discriminant performance with a sensitivity of 86 per cent, a specificity of 83 per cent and an accuracy of 85 per cent (area under the receiver operating characteristic (ROC) curve 0·852). The accuracy of PNN analysis was confirmed in the validation phase on a further 25 subjects; the model correctly assigned 19 patients, giving an overall accuracy of 76 per cent.

CONCLUSION

The pattern of VOCs in patients with colorectal cancer was different from that in healthy controls. The PNN in this study was able to discriminate patients with colorectal cancer with an accuracy of over 75 per cent. Breath VOC analysis appears to have potential clinical application in colorectal cancer screening, although further studies are required to confirm its reliability in heterogeneous clinical settings.

Exhaled molecular profiles in the assessment of cystic fibrosis and primary ciliary dyskinesia

BACKGROUND

Early diagnosis and monitoring of disease activity are essential in cystic fibrosis (CF) and primary ciliary dyskinesia (PCD). We aimed to establish exhaled molecular profiles as the first step in assessing the potential of breath analysis.

METHODS

Exhaled breath was analyzed by electronic nose in 25 children with CF, 25 with PCD and 23 controls. Principle component reduction and canonical discriminant analysis were used to construct internally cross-validated ROC curves.

RESULTS

CF and PCD patients had significantly different breath profiles when compared to healthy controls (CF: sensitivity 84%, specificity 65%; PCD: sensitivity 88%, specificity 52%) and from each other (sensitivity 84%, specificity 60%). Patients with and without exacerbations had significantly different breath profiles (CF: sensitivity 89%, specificity 56%; PCD: sensitivity 100%, specificity 90%).

CONCLUSION

Exhaled molecular profiles significantly differ between patients with CF, PCD and controls. The eNose may have potential in disease monitoring based on the influence of exacerbations on the VOC-profile.

On the features, successes and challenges of selected ion flow tube mass spectrometry

The major features of the selected ion flow tube mass spectrometry (SIFT-MS) analytical method that was conceived and designed for the analysis, in real time, of air obviating sample collections into bags or extraction by pre-concentration of trace compounds onto surfaces are reviewed. The unique analytical capabilities of SIFT-MS for ambient analysis are stressed that allow quantification of volatile organic and inorganic compounds directly from the measurement of physical parameters without the need for regular instrumental calibration using internal or external standards. Then, emphasis is placed on the challenging real-time accurate analysis of single exhalations of humid breath, which is now achieved and readily facilitates wider applications of SIFT-MS in other fields where trace gas analysis has value. The quality of the data obtained by SIFT-MS is illustrated by the quantification of some exhaled breath metabolites that are of immediate relevance to physiology and medicine, including that of hydrogen cyanide in the breath of patients with cystic fibrosis. The current status of SIFT-MS is revealed by a form of a strengths, weakness, opportunities and threats (SWOT) analysis intended to present an objective view of this analytical technique and the likely way forward towards its further development and application.

Effect of transportation and storage using sorbent tubes of exhaled breath samples on diagnostic accuracy of electronic nose analysis

Many (multi-centre) breath-analysis studies require transport and storage of samples. We aimed to test the effect of transportation and storage using sorbent tubes of exhaled breath samples for diagnostic accuracy of eNose and GC-MS analysis. As a reference standard for diagnostic accuracy, breath samples of asthmatic patients and healthy controls were analysed by three eNose devices. Samples were analysed by GC-MS and eNose after 1, 7 and 14 days of transportation and storage using sorbent tubes. The diagnostic accuracy for eNose and GC-MS after storage was compared to the reference standard. As a validation, the stability was assessed of 15 compounds known to be related to asthma, abundant in breath or related to sampling and analysis. The reference test discriminated asthma and healthy controls with a median AUC (range) of 0.77 (0.72-0.76). Similar accuracies were achieved at t1 (AUC eNose 0.78; GC-MS 0.84), t7 (AUC eNose 0.76; GC-MS 0.79) and t14 (AUC eNose 0.83; GC-MS 0.84). The GC-MS analysis of compounds showed an adequate stability for all 15 compounds during the 14 day period. Short-term transportation and storage using sorbent tubes of breath samples does not influence the diagnostic accuracy for discrimination between asthma and health by eNose and GC-MS.

Clinical use of exhaled volatile organic compounds in pulmonary diseases: a systematic review

There is an increasing interest in the potential of exhaled biomarkers, such as volatile organic compounds (VOCs), to improve accurate diagnoses and management decisions in pulmonary diseases. The objective of this manuscript is to systematically review the current knowledge on exhaled VOCs with respect to their potential clinical use in asthma, lung cancer, chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF), and respiratory tract infections. A systematic literature search was performed in PubMed, EMBASE, Cochrane database, and reference lists of retrieved studies. Controlled, clinical, English-language studies exploring the diagnostic and monitoring value of VOCs in asthma, COPD, CF, lung cancer and respiratory tract infections were included. Data on study design, setting, participant characteristics, VOCs techniques, and outcome measures were extracted. Seventy-three studies were included, counting in total 3,952 patients and 2,973 healthy controls. The collection and analysis of exhaled VOCs is non-invasive and could be easily applied in the broad range of patients, including subjects with severe disease and children. Various research groups demonstrated that VOCs profiles could accurately distinguish patients with a pulmonary disease from healthy controls. Pulmonary diseases seem to be characterized by a disease specific breath-print, as distinct profiles were found in patients with dissimilar diseases. The heterogeneity of studies challenged the inter-laboratory comparability. In conclusion, profiles of VOCs are potentially able to accurately diagnose various pulmonary diseases. Despite these promising findings, multiple challenges such as further standardization and validation of the diverse techniques need to be mastered before VOCs can be applied into clinical practice.

Detection of Pseudomonas aeruginosa in sputum headspace through volatile organic compound analysis

INTRODUCTION

Chronic pulmonary infection is the hallmark of cystic fibrosis lung disease. Searching for faster and easier screening may lead to faster diagnosis and treatment of Pseudomonas aeruginosa (P. aeruginosa). Our aim was to analyze and build a model to predict the presence of P. aeruginosa in sputa.

METHODS

Sputa from 28 bronchiectatic patients were used for bacterial culturing and analysis of volatile compounds by gas chromatography-mass spectrometry. Data analysis and model building were done by Partial Least Squares Regression Discriminant analysis (PLS-DA). Two analysis were performed: one comparing P. aeruginosa positive with negative cultures at study visit (PA model) and one comparing chronic colonization according to the Leeds criteria with P. aeruginosa negative patients (PACC model).

RESULTS

The PA model prediction of P. aeruginosa presence was rather poor, with a high number of false positives and false negatives. On the other hand, the PACC model was stable and explained chronic P. aeruginosa presence for 95% with 4 PLS-DA factors, with a sensitivity of 100%, a positive predictive value of 86% and a negative predictive value of 100%.

CONCLUSION

Our study shows the potential for building a prediction model for the presence of chronic P. aeruginosa based on volatiles from sputum.

Machine learning methods on exhaled volatile organic compounds for distinguishing COPD patients from healthy controls

Exhaled volatile organic compounds (VOCs) have shown promise in diagnosing chronic obstructive pulmonary disease (COPD) but studies have been limited by small sample size and potential confounders. An investigation was conducted in order to establish whether combinations of VOCs could identify COPD patients from age and BMI matched controls. Breath samples were collected from 119 stable COPD patients and 63 healthy controls. The samples were collected with a portable apparatus, and then assayed by gas chromatography and mass spectroscopy. Machine learning approaches were applied to the data and the automatically generated models were assessed using classification accuracy and receiver operating characteristic (ROC) curves. Cross-validation of the combinations correctly predicted the diagnosis in 79% of COPD patients and 64% of controls and an optimum area under the ROC curve of 0.82 was obtained. Comparison of current and ex smokers within the COPD group showed that smoking status was likely to affect the classification; with correct prediction of smoking status in 85% of COPD subjects. When current smokers were omitted from the analysis, prediction of COPD was similar at 78% but correct prediction of controls was increased to 74%. Applying different analytical methods to the largest group of subjects so far, suggests VOC analysis holds promise for diagnosing COPD but smoking status needs to be balanced.

Exhaled breath analysis with a colorimetric sensor array for the identification and characterization of lung cancer

INTRODUCTION

The pattern of exhaled breath volatile organic compounds represents a metabolic biosignature with the potential to identify and characterize lung cancer. Breath biosignature-based classification of homogeneous subgroups of lung cancer may be more accurate than a global breath signature. Combining breath biosignatures with clinical risk factors may improve the accuracy of the signature.

OBJECTIVES

To develop an exhaled breath biosignature of lung cancer using a colorimetric sensor array and to determine the accuracy of breath biosignatures of lung cancer characteristics with and without the inclusion of clinical risk factors.

METHODS

The exhaled breath of 229 study subjects, 92 with lung cancer and 137 controls, was drawn across a colorimetric sensor array. Logistic prediction models were developed and statistically validated based on the color changes of the sensor. Age, sex, smoking history, and chronic obstructive pulmonary disease were incorporated in the prediction models.

RESULTS

The validated prediction model of the combined breath and clinical biosignature was moderately accurate at distinguishing lung cancer from control subjects (C-statistic 0.811). The accuracy improved when the model focused on only one histology (C-statistic 0.825-0.890). Individuals with different histologies could be accurately distinguished from one another (C-statistic 0.864 for adenocarcinoma versus squamous cell carcinoma). Moderate accuracies were noted for validated breath biosignatures of stage and survival (C-statistic 0.785 and 0.693, respectively).

CONCLUSIONS

A colorimetric sensor array is capable of identifying exhaled breath biosignatures of lung cancer. The accuracy of breath biosignatures can be optimized by evaluating specific histologies and incorporating clinical risk factors.

Comparison of metabolites in exhaled breath and bronchoalveolar lavage fluid samples in a mouse model of asthma

BACKGROUND

A multi-capillary column ion mobility spectrometer (MCC/IMS) was developed to provide a method for the noninvasive diagnosis of lung diseases. The possibility of measuring the exhaled breath of mice was evaluated previously. The aim of the present study was to reveal whether mice affected by airway inflammation can be identified via MCC/IMS.

METHODS

Ten mice were sensitized and challenged with ovalbumin to induce allergic airway inflammation. The breath and volatile compounds of bronchoalveolar lavage fluid (BALF) were measured by MCC/IMS. Furthermore, histamine, nitric oxide, and arachidonic acid were determined as inflammatory markers in vitro.

RESULTS

Six volatile molecules were found in the BALF headspace at a significantly higher concentration in mice with airway inflammation compared with healthy animals. The concentration of substances correlated with the numbers of infiltrating eosinophilic granulocytes. However, substances showing a significantly different concentration in the BALF headspace were not found to be different in exhaled breath. Histamine and nitric oxide were identified by MCC/IMS in vitro but not in the BALF headspace or exhaled breath.

CONCLUSION

Airway inflammation in mice is detectable by the analysis of the BALF headspace via MCC/IMS. Molecules detected in the BALF headspace of asthmatic mice at a higher concentration than in healthy animals may originate from oxidative stress induced by airway inflammation. As already described for humans, we found no correlation between the biomarker concentration in the BALF and the breath of mice. We suggest using the model described here to gain deeper insights into this discrepancy.

Electronic nose breathprints are independent of acute changes in airway caliber in asthma

Molecular profiling of exhaled volatile organic compounds (VOC) by electronic nose technology provides breathprints that discriminate between patients with different inflammatory airway diseases, such as asthma and COPD. However, it is unknown whether this is determined by differences in airway caliber. We hypothesized that breathprints obtained by electronic nose are independent of acute changes in airway caliber in asthma. Ten patients with stable asthma underwent methacholine provocation (Visit 1) and sham challenge with isotonic saline (Visit 2). At Visit 1, exhaled air was repetitively collected pre-challenge, after reaching the provocative concentration (PC(20)) causing 20% fall in forced expiratory volume in 1 second (FEV(1)) and after subsequent salbutamol inhalation. At Visit 2, breath was collected pre-challenge, post-saline and post-salbutamol. At each occasion, an expiratory vital capacity was collected after 5 min of tidal breathing through an inspiratory VOC-filter in a Tedlar bag and sampled by electronic nose (Cyranose 320). Breathprints were analyzed with principal component analysis and individual factors were compared with mixed model analysis followed by pairwise comparisons. Inhalation of methacholine led to a 30.8 ± 3.3% fall in FEV(1) and was followed by a significant change in breathprint (p = 0.04). Saline inhalation did not induce a significant change in FEV(1), but altered the breathprint (p = 0.01). However, the breathprint obtained after the methacholine provocation was not significantly different from that after saline challenge (p = 0.27). The molecular profile of exhaled air in patients with asthma is altered by nebulized aerosols, but is not affected by acute changes in airway caliber. Our data demonstrate that breathprints by electronic nose are not confounded by the level of airway obstruction.

Alignment of retention time obtained from multicapillary column gas chromatography used for VOC analysis with ion mobility spectrometry

Multicapillary column (MCC) ion mobility spectrometers (IMS) are increasingly in demand for medical diagnosis, biological applications and process control. In a MCC-IMS, volatile compounds are differentiated by specific retention time and ion mobility when rapid preseparation techniques are applied, e.g. for the analysis of complex and humid samples. Therefore, high accuracy in the determination of both parameters is required for reliable identification of the signals. The retention time in the MCC is the subject of the present investigation because, for such columns, small deviations in temperature and flow velocity may cause significant changes in retention time. Therefore, a universal correction procedure would be a helpful tool to increase the accuracy of the data obtained from a gas-chromatographic preseparation. Although the effect of the carrier gas flow velocity and temperature on retention time is not linear, it could be demonstrated that a linear alignment can compensate for the changes in retention time due to common minor deviations of both the carrier gas flow velocity and the column temperature around the MCC-IMS standard operation conditions. Therefore, an effective linear alignment procedure for the correction of those deviations has been developed from the analyses of defined gas mixtures under various experimental conditions. This procedure was then applied to data sets generated from real breath analyses obtained in clinical studies using different instruments at different measuring sites for validation. The variation in the retention time of known signals, especially for compounds with higher retention times, was significantly improved. The alignment of the retention time—an indispensable procedure to achieve a more precise identification of analytes—using the proposed method reduces the random error caused by small accidental deviations in column temperature and flow velocity significantly.