Breath testing for SARS-CoV-2 infection.

BACKGROUND: From a public health perspective, the identification of individuals with mild respiratory symptoms due to SARS-CoV-2 infection is important to contain the spread of the disease. The objective of this study was to identify volatile organic compounds (VOCs) in exhaled breath common to infection with different variants of the SARS-CoV-2 virus to inform the development of a point-of-care breath test to detect infected individuals with mild symptoms. METHODS: A prospective, real-world, observational study was conducted on mildly symptomatic out-patients presenting to community test-sites for RT-qPCR SARS-CoV-2 testing when the Alpha, Beta, and Delta variants were driving the COVID-19 pandemic. VOCs in exhaled breath were compared between PCR-positive and negative individuals using TD-GC-ToF-MS. Candidate VOCs were tested in an independent set of samples collected during the Omicron phase of the pandemic. FINDINGS: Fifty breath samples from symptomatic RT-qPCR positive and 58 breath samples from test-negative, but symptomatic participants were compared. Of the 50 RT-qPCR-positive participants, 22 had breath sampling repeated 8-12 weeks later. PCA-X model yielded 12 distinct VOCs that discriminated SARS-CoV-2 active infection compared to recovery/convalescence period, with an area under the receiver operator characteristic curve (AUROC), of 0.862 (0.747-0.977), sensitivity, and specificity of 82% and 86%, respectively. PCA-X model from 50 RT-qPCR positive and 58 negative symptomatic participants, yielded 11 VOCs, with AUROC of 0.72 (0.604-0.803) and sensitivity of 72%, specificity 65.5%. The 11 VOCs were validated in a separate group of SARS-CoV-2 Omicron positive patients' vs healthy controls demonstrating an AUROC of 0.96 (95% CI 0.827-0.993) with sensitivity of 80% specificity of 90%. INTERPRETATION: Exhaled breath analysis is a promising non-invasive, point-of-care method to detect mild COVID-19 infection. FUNDING: Funding for this study was a competitive grant awarded from the Vancouver Coastal Research Institute as well as funding from the BC Cancer Foundation.

Peppermint protocol: first results for gas chromatography-ion mobility spectrometry.

ThePeppermint Initiativeseeks to inform the standardisation of breath analysis methods. FivePeppermint Experimentswith gas chromatography-ion mobility spectrometry (GC-IMS), operating in the positive mode with a tritium3H 5.68 keV, 370 MBq ionisation source, were undertaken to provide benchmarkPeppermint Washoutdata for this technique, to support its use in breath-testing, analysis, and research. Headspace analysis of a peppermint-oil capsule by GC-IMS with on-column injection (0.5 cm3) identified 12 IMS responsive compounds, of which the four most abundant were: eucalyptol;ß-pinene;α-pinene; and limonene. Elevated concentrations of these four compounds were identified in exhaled-breath following ingestion of a peppermint-oil capsule. An unidentified compound attributed as a volatile catabolite of peppermint-oil was also observed. The most intense exhaled peppermint-oil component was eucalyptol, which was selected as a peppermint marker for benchmarking GC-IMS. Twenty-five washout experiments monitored levels of exhaled eucalyptol, by GC-IMS with on-column injection (0.5 cm3), att= 0 min, and then att+ 60,t+ 90,t+ 165,t+ 285 andt+ 360 min from ingestion of a peppermint capsule resulting in 148 peppermint breath analyses. Additionally, thePeppermint Washoutdata was used to evaluate clinical deployments with a further five washout tests run in clinical settings generating an additional 35 breath samples. Regression analysis yielded an average extrapolated time taken for exhaled eucalyptol levels to return to baseline values to be 429 ± 62 min (±95% confidence-interval). The benchmark value was assigned to the lower 95% confidence-interval, 367 min. Further evaluation of the data indicated that the maximum number of volatile organic compounds discernible from a 0.5 cm3breath sample was 69, while the use of an in-line biofilter appeared to reduce this to 34.

Fast and automated biomarker detection in breath samples with machine learning.

Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions and can be used for fast, accurate and non-invasive diagnostics. Gas chromatography-mass spectrometry (GC-MS) is used to measure VOCs, but its application is limited by expert-driven data analysis that is time-consuming, subjective and may introduce errors. We propose a machine learning-based system to perform GC-MS data analysis that exploits deep learning pattern recognition ability to learn and automatically detect VOCs directly from raw data, thus bypassing expert-led processing. We evaluate this new approach on clinical samples and with four types of convolutional neural networks (CNNs): VGG16, VGG-like, densely connected and residual CNNs. The proposed machine learning methods showed to outperform the expert-led analysis by detecting a significantly higher number of VOCs in just a fraction of time while maintaining high specificity. These results suggest that the proposed novel approach can help the large-scale deployment of breath-based diagnosis by reducing time and cost, and increasing accuracy and consistency.

The Impact of a Graded Maximal Exercise Protocol on Exhaled Volatile Organic Compounds: A Pilot Study.

Exhaled volatile organic compounds (VOCs) are of interest due to their minimally invasive sampling procedure. Previous studies have investigated the impact of exercise, with evidence suggesting that breath VOCs reflect exercise-induced metabolic activity. However, these studies have yet to investigate the impact of maximal exercise to exhaustion on breath VOCs, which was the main aim of this study. Two-litre breath samples were collected onto thermal desorption tubes using a portable breath collection unit. Samples were collected pre-exercise, and at 10 and 60 min following a maximal exercise test (VO2MAX). Breath VOCs were analysed by thermal desorption-gas chromatography-mass spectrometry using a non-targeted approach. Data showed a tendency for reduced isoprene in samples at 10 min post-exercise, with a return to baseline by 60 min. However, inter-individual variation meant differences between baseline and 10 min could not be confirmed, although the 10 and 60 min timepoints were different (p = 0.041). In addition, baseline samples showed a tendency for both acetone and isoprene to be reduced in those with higher absolute VO2MAX scores (mL(O2)/min), although with restricted statistical power. Baseline samples could not differentiate between relative VO2MAX scores (mL(O2)/kg/min). In conclusion, these data support that isoprene levels are dynamic in response to exercise.

The variability of volatile organic compounds in the indoor air of clinical environments.

The development of clinical breath-analysis is confounded by the variability of background volatile organic compounds (VOCs). Reliable interpretation of clinical breath-analysis at individual, and cohort levels requires characterisation of clinical-VOC levels and exposures. Active-sampling with thermal-desorption/gas chromatography-mass spectrometry recorded and evaluated VOC concentrations in 245 samples of indoor air from three sites in a large National Health Service (NHS) provider trust in the UK over 27 months. Data deconvolution, alignment and clustering isolated 7344 features attributable to VOC and described the variability (composition and concentration) of respirable clinical VOC. 328 VOC were observed in more than 5% of the samples and 68 VOC appeared in more than 30% of samples. Common VOC were associated with exogenous and endogenous sources and 17 VOC were identified as seasonal differentiators. The presence of metabolites from the anaesthetic sevoflurane, and putative-disease biomarkers in room air, indicated that exhaled VOC were a source of background-pollution in clinical breath-testing activity. With the exception of solvents, and waxes associated with personal protective equipment (PPE), exhaled VOC concentrations above 3µg m-3are unlikely to arise from room air contamination, and in the absence of extensive survey-data, this level could be applied as a threshold for inclusion in studies, removing a potential environmental confounding-factor in developing breath-based diagnostics.

IABR Symposium 2021 meeting report: breath standardization, sampling, and testing in a time of COVID-19.

Due to COVID-19 travel disruptions, the International Association of Breath Research hosted the planned 2021 Breath Summit virtually as a symposium with oral and poster presentations. The event was comprised of a week-long social media asynchronous online event for sharing research abstracts, posters and discussions. Subsequently, there were two days of real-time webinar platform interactions each featuring three technical presentations, open forum questions, answers, and commentary. The symposium was well attended and well received. It allowed the breath community to share new research and to reconnect with colleagues and friends. This report presents an overview of the topics presented and various salient discussion points.

Volatile organic compounds in a headspace sampling system and asthmatics sputum samples.

The headspace of a biological sample contains exogenous volatile organic compounds (VOCs) present within the sampling environment which represent the background signal. This study aimed to characterise the background signal generated from a headspace sampling system in a clinical site, to evaluate intra- and inter-day variation of background VOC and to understand the impact of a sample itself upon commonly reported background VOC using sputum headspace samples from severe asthmatics. The headspace, in absence of a biological sample, was collected hourly from 11am to 3pm within a day (time of clinical samples acquisition), and from Monday to Friday in a week, and analysed by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). Chemometric analysis identified 1120 features, 37 of which were present in at least the 80% of all the samples. The analyses of intra- and inter-day background variations were performed on 13 of the most abundant features, ubiquitously present in headspace samples. The concentration ratios relative to background were reported for the selected abundant VOC in 36 asthmatic sputum samples, acquired from 36 stable severe asthma patients recruited at Glenfield Hospital, Leicester, UK. The results identified no significant intra- or inter-day variations in compounds levels and no systematic bias ofz-scores, with the exclusion of benzothiazole, whose abundance increased linearly between 11am and 3pm with a maximal intra-day fold change of 2.13. Many of the identified background features are reported in literature as components of headspace of biological samples and are considered potential biomarkers for several diseases. The selected background features were identified in headspace of all severe asthma sputum samples, albeit with varying levels of enrichment relative to background. Our observations support the need to consider the background signal derived from the headspace sampling system when developing and validating headspace biomarker signatures using clinical samples.

Diagnosis of COVID-19 by analysis of breath with gas chromatography-ion mobility spectrometry - a feasibility study.

BACKGROUND: There is an urgent need to rapidly distinguish COVID-19 from other respiratory conditions, including influenza, at first-presentation. Point-of-care tests not requiring laboratory- support will speed diagnosis and protect health-care staff. We studied the feasibility of using breath-analysis to distinguish these conditions with near-patient gas chromatography-ion mobility spectrometry (GC-IMS). METHODS: Independent observational prevalence studies at Edinburgh, UK, and Dortmund, Germany, recruited adult patients with possible COVID-19 at hospital presentation. Participants gave a single breath-sample for VOC analysis by GC-IMS. COVID-19 infection was identified by transcription polymerase chain reaction (RT- qPCR) of oral/nasal swabs together with clinical-review. Following correction for environmental contaminants, potential COVID-19 breath-biomarkers were identified by multi-variate analysis and comparison to GC-IMS databases. A COVID-19 breath-score based on the relative abundance of a panel of volatile organic compounds was proposed and tested against the cohort data. FINDINGS: Ninety-eight patients were recruited, of whom 21/33 (63.6%) and 10/65 (15.4%) had COVID-19 in Edinburgh and Dortmund, respectively. Other diagnoses included asthma, COPD, bacterial pneumonia, and cardiac conditions. Multivariate analysis identified aldehydes (ethanal, octanal), ketones (acetone, butanone), and methanol that discriminated COVID-19 from other conditions. An unidentified-feature with significant predictive power for severity/death was isolated in Edinburgh, while heptanal was identified in Dortmund. Differentiation of patients with definite diagnosis (25 and 65) of COVID-19 from non-COVID-19 was possible with 80% and 81.5% accuracy in Edinburgh and Dortmund respectively (sensitivity/specificity 82.4%/75%; area-under-the-receiver- operator-characteristic [AUROC] 0.87 95% CI 0.67 to 1) and Dortmund (sensitivity / specificity 90%/80%; AUROC 0.91 95% CI 0.87 to 1). INTERPRETATION: These two studies independently indicate that patients with COVID-19 can be rapidly distinguished from patients with other conditions at first healthcare contact. The identity of the marker compounds is consistent with COVID-19 derangement of breath-biochemistry by ketosis, gastrointestinal effects, and inflammatory processes. Development and validation of this approach may allow rapid diagnosis of COVID-19 in the coming endemic flu seasons. FUNDING: MR was supported by an NHS Research Scotland Career Researcher Clinician award. DMR was supported by the University of Edinburgh ref COV_29.

Breath markers for therapeutic radiation.

Radiation dose is important in radiotherapy. Too little, and the treatment is not effective, too much causes radiation toxicity. A biochemical measurement of the effect of radiotherapy would be useful in personalisation of this treatment. This study evaluated changes in exhaled breath volatile organic compounds (VOC) associated with radiotherapy with thermal desorption gas chromatography mass-spectrometry followed by data processing and multivariate statistical analysis. Further the feasibility of adopting gas chromatography ion mobility spectrometry for radiotherapy point-of-care breath was assessed. A total of 62 participants provided 240 end-tidal 1 dm3 breath samples before radiotherapy and at 1, 3, and 6 h post-exposure, that were analysed by thermal-desorption/gas-chromatography/quadrupole mass-spectrometry. Data were registered by retention-index and mass-spectra before multivariate statistical analyses identified candidate markers. A panel of sulfur containing compounds (thio-VOC) were observed to increase in concentration over the 6 h following irradiation. 3-methylthiophene (80 ng.m-3 to 790 ng.m-3) had the lowest abundance while 2-thiophenecarbaldehyde(380 ng.m-3 to 3.85 µg.m-3) the highest; note, exhaled 2-thiophenecarbaldehyde has not been observed previously. The putative tumour metabolite 2,4-dimethyl-1-heptene concentration reduced by an average of 73% over the same time. Statistical scoring based on the signal intensities thio-VOC and 3-methylthiophene appears to reflect individuals' responses to radiation exposure from radiotherapy. The thio-VOC are hypothesised to derive from glutathione and Maillard-based reactions and these are of interest as they are associated with radio-sensitivity. Further studies with continuous monitoring are needed to define the development of the breath biochemistry response to irradiation and to determine the optimum time to monitor breath for radiotherapy markers. Consequently, a single 0.5 cm3 breath-sample gas chromatography-ion mobility approach was evaluated. The calibrated limit of detection for 3-methylthiophene was 10 µg.m-3 with a lower limit of the detector's response estimated to be 210 fg.s-1; the potential for a point-of-care radiation exposure study exists.

A benchmarking protocol for breath analysis: the peppermint experiment.

Sampling of volatile organic compounds (VOCs) has shown promise for detection of a range of diseases but results have proved hard to replicate due to a lack of standardization. In this work we introduce the 'Peppermint Initiative'. The initiative seeks to disseminate a standardized experiment that allows comparison of breath sampling and data analysis methods. Further, it seeks to share a set of benchmark values for the measurement of VOCs in breath. Pilot data are presented to illustrate the standardized approach to the interpretation of results obtained from the Peppermint experiment. This pilot study was conducted to determine the washout profile of peppermint compounds in breath, identify appropriate sampling time points, and formalise the data analysis. Five and ten participants were recruited to undertake a standardized intervention by ingesting a peppermint oil capsule that engenders a predictable and controlled change in the VOC profile in exhaled breath. After collecting a pre-ingestion breath sample, five further samples are taken at 2, 4, 6, 8, and 10 h after ingestion. Samples were analysed using ion mobility spectrometry coupled to multi-capillary column and thermal desorption gas chromatography mass spectrometry. A regression analysis of the washout data was used to determine sampling times for the final peppermint protocol, and the time for the compound measurement to return to baseline levels was selected as a benchmark value. A measure of the quality of the data generated from a given technique is proposed by comparing data fidelity. This study protocol has been used for all subsequent measurements by the Peppermint Consortium (16 partners from seven countries). So far 1200 breath samples from 200 participants using a range of sampling and analytical techniques have been collected. The data from the consortium will be disseminated in subsequent technical notes focussing on results from individual platforms.

Sensors' array of aspiration ion mobility spectrometer as a tool for bacteria discrimination.

The possibility of achieving bacterial discrimination using a miniaturized aspiration ion mobility spectrometer model ChemPro-100i (Environics Oy) has been tested by interrogating the headspace air samples above in vitro bacterial cultures of three species - Escherichia coli, Bacillus subtilis and Staphylococcus aureus, respectively. The ChemPro-100i highly integrated seven sensor array, composed of one a-IMS cell, three MOS (metal oxide sensors), one FET (field effect transistor) sensor and two SC (semiconductor) sensors, provided enough analytical information to discriminate between the three bacterial species. Statistical data processing using either principal component analysis (PCA) or partial least squares discriminant analysis (PLS-DA) was accomplished. We concluded that although the data from the aspiration-type ion mobility sensor, with its 16 ion detectors, is absolutely sufficient to discriminate between various bacteria using their volatile compounds' chemical profile, the other six sensors deliver additional, valuable information.

VOCCluster: Untargeted Metabolomics Feature Clustering Approach for Clinical Breath Gas Chromatography/Mass Spectrometry Data.

Metabolic profiling of breath analysis involves processing, alignment, scaling, and clustering of thousands of features extracted from gas chromatography/mass spectrometry (GC/MS) data from hundreds of participants. The multistep data processing is complicated, operator error-prone, and time-consuming. Automated algorithmic clustering methods that are able to cluster features in a fast and reliable way are necessary. These accelerate metabolic profiling and discovery platforms for next-generation medical diagnostic tools. Our unsupervised clustering technique, VOCCluster, prototyped in Python, handles features of deconvolved GC/MS breath data. VOCCluster was created from a heuristic ontology based on the observation of experts undertaking data processing with a suite of software packages. VOCCluster identifies and clusters groups of volatile organic compounds (VOCs) from deconvolved GC/MS breath with similar mass spectra and retention index profiles. VOCCluster was used to cluster more than 15 000 features extracted from 74 GC/MS clinical breath samples obtained from participants with cancer before and after a radiation therapy. Results were evaluated against a panel of ground truth compounds and compared to other clustering methods (DBSCAN and OPTICS) that were used in previous metabolomics studies. VOCCluster was able to cluster those features into 1081 groups (including endogenous and exogenous compounds and instrumental artifacts) with an accuracy rate of 96% (±0.04 at 95% confidence interval).

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

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

Discrimination of bacteria by rapid sensing their metabolic volatiles using an aspiration-type ion mobility spectrometer (a-IMS) and gas chromatography-mass spectrometry GC-MS.

The objective of our study was to investigate whether one may quickly and reliably discriminate different microorganism strains by direct monitoring of the headspace atmosphere above their cultures. Headspace samples above a series of in vitro bacterial cultures were directly interrogated using an aspiration type ion mobility spectrometer (a-IMS), which produced distinct profiles ("fingerprints") of ion currents generated simultaneously by the detectors present inside the ion mobility cell. Data processing and analysis using principal component analysis showed net differences in the responses produced by volatiles emitted by various bacterial strains. Fingerprint assignments were conferred on the basis of product ion mobilities; ions of differing size and mass were deflected in a different degree upon their introduction of a transverse electric field, impacting finally on a series of capacitors (denominated as detectors, or channels) placed in a manner analogous to sensor arrays. Three microorganism strains were investigated - Escherichia coli, Bacillus subtilis and Staphylococcus aureus; all strains possess a relatively low pathogenic character. Samples of air with a 5 cm3 volume from the headspace above the bacterial cultures in agar growth medium were collected using a gas-tight chromatographic syringe and injected inside the closed-loop pneumatic circuit of the breadboard a-IMS instrument model ChemPro-100i (Environics Oy, Finland), at a distance of about 1 cm from the ionization source. The resulting chemical fingerprints were produced within two seconds from the moment of injection. The sampling protocol involved to taking three replicate samples from each of 10 different cultures for a specific strain, during a total period of 72 h after the initial incubation - at 24, 48 and 72 h, respectively. Principal component analysis (PCA) was used to discriminate between the IMS fingerprints. PCA was found to successfully discriminate between bacteria at three levels in the experimental campaign: 1) between blank samples from growth medium and samples from bacterial cultures, 2) between samples from different bacterial strains, and 3) between time evolutions of headspace samples from the same bacterial strain over the 3-day sampling period. Consistent classification between growth medium samples and growth medium inoculated with bacteria was observed in both positive and negative detection/ionization modes. In parallel, headspace air samples of 1 dm3 were collected from each bacterial culture and loaded onto Tenax™-Carbograph desorption tubes, using a custom built sampling unit based on a portable sampling pump. One sample was taken for each of 10 different cultures of a strain, at 24, 48 and 72 h after the initial incubation. These adsorption tubes were subsequently analyzed using thermal desorption - gas chromatography - mass spectrometry (TD-GC-MS). This second dataset was intended to produce a qualitative analysis of the volatiles present in the headspace above the bacterial cultures.

Spatial variations in the microbial community structure and diversity of the human foot is associated with the production of odorous volatiles.

The human foot provides an ideal environment for the colonization and growth of bacteria and subsequently is a body site associated with the liberation of odour. This study aimed to enumerate and spatially map bacterial populations' resident across the foot to understand any association with odour production. Culture-based analysis confirmed that Staphylococci were present in higher numbers than aerobic corynebacteria and Gram-positive aerobic cocci, with all species being present at much higher levels on the plantar sites compared to dorsal sites. Microbiomic analysis supported these findings demonstrating that Staphylococcus spp. were dominant across different foot sites and comprised almost the entire bacterial population on the plantar surface. The levels of volatile fatty acids, including the key foot odour compound isovaleric acid, that contribute to foot odour were significantly increased at the plantar skin site compared to the dorsal surface. The fact that isovaleric acid was not detected on the dorsal surface but was present on the plantar surface is probably attributable to the high numbers of Staphylococcus spp. residing at this site. Variations in the spatial distribution of these microbes appear to be responsible for the localized production of odour across the foot.

High throughput volatile fatty acid skin metabolite profiling by thermal desorption secondary electrospray ionisation mass spectrometry.

The non-invasive nature of volatile organic compound (VOC) sampling from skin makes this a priority in the development of new screening and diagnostic assays. Evaluation of recent literature highlights the tension between the analytical utility of ambient ionisation approaches for skin profiling and the practicality of undertaking larger campaigns (higher statistical power), or undertaking research in remote locations. This study describes how VOC may be sampled from skin and recovered from a polydimethylsilicone sampling coupon and analysed by thermal desorption (TD) interfaced to secondary electrospray ionisation (SESI) time-of-flight mass spectrometry (MS) for the high throughput screening of volatile fatty acids (VFAs) from human skin. Analysis times were reduced by 79% compared to gas chromatography-mass spectrometry methods (GC-MS) and limits of detection in the range 300 to 900 pg cm(-2) for VFA skin concentrations were obtained. Using body odour as a surrogate model for clinical testing 10 Filipino participants, 5 high and 5 low odour, were sampled in Manilla and the samples returned to the UK and screened by TD-SESI-MS and TD-GC-MS for malodour precursors with greater than >95% agreement between the two analytical techniques. Eight additional VFAs were also identified by both techniques with chains 4 to 15 carbons long being observed. TD-SESI-MS appears to have significant potential for the high throughput targeted screening of volatile biomarkers in human skin.

Dynamic vapor generator that simulates transient odor emissions of victims entrapped in the voids of collapsed buildings.

The design, development, and validation of a dynamic vapor generator are presented. The generator simulates human scent (odor) emissions from trapped victims in the voids of collapsed buildings. The validation of the device was carried out using a reference detector: a quadrupole mass spectrometer equipped with a pulsed sampling (PS-MS) system. A series of experiments were conducted for evaluating the simulator's performance, defining types and weights of different factors, and proposing further optimization of the device. The developed device enabled the production of stable and transient odor profiles in a controllable and reproducible way (relative standard deviation, RSD < 11%) at ppbv to low ppmv concentrations and allowed emission durations up to 30 min. Moreover, the factors affecting its optimum performance (i.e., evaporation chamber temperature, air flow rate through the mixing chamber, air flow rate through the evaporation chamber, and type of compound) were evaluated through an analysis of variance (ANOVA) tool revealing the next steps toward optimizing the generator. The developed simulator, potentially, can also serve the need for calibrating and evaluating the performance of analytical devices (e.g., gas chromatographers, ion mobility spectrometers, mass spectrometers, sensors, e-noses) in the field. Furthermore, it can contribute in better training of urban search and rescue (USaR) canines.

Gas-phase and solution studies of three resorcin[4]arene derivatives using electrospray time-of-flight mass spectrometry.

Electrospray ionisation mass spectrometry (ESI-MS) has been used to study the relative gas-phase proton and alkali metal (Li, Na, K and Cs) binding affinities of three different resorcin[4]arenes using the kinetic method. Collision-induced dissociation (CID) was used to study the fragmentation of resorcin[4]arene heterodimer sandwich complexes, allowing the relative binding affinity order to be established. All the alkali metal cations have the same gas-phase binding affinity order with the resorcin[4]arene host molecules. At collision energies of > or = 13eV, one of the [resorcin[4]arene+Metal]+, (Metal = Li, Na, K) ions fragmented through break-up of the resorcin[4]arene, whilst the other host resorcin[4]arene remained intact, causing an apparent change in binding affinity at high collision energy. This effect was not observed with caesium, since all complex ions dissociated readily under CID by displacement of the caesium cation. The binding affinity for the protonated resorcin[4]arenes was found to be different from the alkali metal cation binding affinity because of the higher proton affinity of the nitrogen-containing resorcin[4]arenes. It is shown that resorcin[4]arenes containing an oxazine ring can be converted into a ring-opened derivative via an Eschweiler-CLarke reaction in the presence of formic acid. A second ring-opening process also occurs, including a hydrolysis reaction that results in apparent Losses of 12 mass units from the intact resorcin[4]arene. Both these reactions occur in solution before mass spectrometric investigation and cannot be achieved by CID. This observation was confirmed by inducing the Eschweiter-CLarke reaction in a model benzoxazine compound.

Detection of metabolites of trapped humans using ion mobility spectrometry coupled with gas chromatography.

For the first time, ion mobility spectrometry coupled with rapid gas chromatography, using multicapillary columns, was applied for the development of a pattern of signs of life for the localization of entrapped victims after disaster events (e.g., earthquake, terroristic attack). During a simulation experiment with entrapped volunteers, 12 human metabolites could be detected in the air of the void with sufficient sensitivity to enable a valid decision on the presence of a living person. Using a basic normalized summation of the measured concentrations, all volunteers involved in the particular experiments could be recognized only few minutes after they entered the simulation void and after less than 3 min of analysis time. An additional independent validation experiment enabled the recognition of a person in a room of ∼25 m(3) after ∼30 min with sufficiently high sensitivity to detect even a person briefly leaving the room. Undoubtedly, additional work must be done on analysis time and weight of the equipment, as well as on validation during real disaster events. However, the enormous potential of the method as a significantly helpful tool for search-and-rescue operations, in addition to trained canines, could be demonstrated.

A workflow for the metabolomic/metabonomic investigation of exhaled breath using thermal desorption GC-MS.

BACKGROUND: Confounding factors in the analysis of human breath by thermal desorption GC-MS are reviewed, with special emphasis on the high water levels encountered in human breath samples. RESULTS: Multilinear regression optimization of breath sampling factors, along with the selection of ubiquitous sample components used as retention-time standards, enabled data registration based on retention indexing and mass spectral alignment. This was done on a component-by-component basis. The methodology developed reconciled participant safety, artefacts from accelerated hydrolysis of the stationary phase and the destructive nature of thermal desorption. Furthermore, using ubiquitous methylated cyclic-siloxanes in the thermal desorption-GC-MS chromatograms enabled secondary retention indexing for each chromatogram. This methodology enables the creation of a 'breath matrix' that is based on a combination of retention indexing and the mass spectral registration of isolated peaks. CONCLUSION: This approach facilitated more efficient data modeling and a case study from a 22-participant (10 male, 12 female) stress-intervention experiment. Principal component analysis of data registered by retention indexing did not classify successfully stressed from unstressed states. By contrast, adoption of a breath matrix approach enabled 95% separation.