Background Breath volatile organic compounds (VOCs) may be useful for asthma diagnosis and phenotyping, identifying patients who could benefit from personalised therapeutic strategies. The authors aimed to identify specific patterns of breath VOCs in patients with asthma and in clinically relevant disease phenotypes. Methods Breath samples were analysed by gas chromatographyemass spectrometry. The Asthma Control Questionnaire was completed, together with lung function and induced sputum cell counts. Breath data were reduced to principal components, and these principal components were used in multiple logistic regression to identify discriminatory models for diagnosis, sputum inflammatory cell profile and asthma control. Results The authors recruited 35 patients with asthma and 23 matched controls. A model derived from 15 VOCs classified patients with asthma with an accuracy of 86%, and positive and negative predictive values of 0.85 and 0.89, respectively. Models also classified patients with asthma based on the following phenotypes: sputum (obtained in 18 patients with asthma) eosinophilia $2% area under the receiver operating characteristics (AUROC) curve 0.98, neutrophilia $40% AUROC 0.90 and uncontrolled asthma (Asthma Control Questionnaire $1) AUROC 0.96. Conclusions Detection of characteristic breath VOC profiles could classify patients with asthma versus controls, and clinically relevant disease phenotypes based on sputum inflammatory profile and asthma control. Prospective validation of these models may lead to clinical application of non-invasive breath profiling in asthma.

BackgroundNon-invasive phenotyping of chronic respiratory diseases would be highly beneficial in the personalised medicine of the future. Volatile organic compounds can be measured in the exhaled breath and may be produced or altered by disease processes. We investigated whether distinct patterns of these compounds were present in chronic obstructive pulmonary disease (COPD) and clinically relevant disease phenotypes.MethodsBreath samples from 39 COPD subjects and 32 healthy controls were collected and analysed using gas chromatography time-of-flight mass spectrometry. Subjects with COPD also underwent sputum induction. Discriminatory compounds were identified by univariate logistic regression followed by multivariate analysis: 1. principal component analysis; 2. multivariate logistic regression; 3. receiver operating characteristic (ROC) analysis.ResultsComparing COPD versus healthy controls, principal component analysis clustered the 20 best-discriminating compounds into four components explaining 71% of the variance. Multivariate logistic regression constructed an optimised model using two components with an accuracy of 69%. The model had 85% sensitivity, 50% specificity and ROC area under the curve of 0.74. Analysis of COPD subgroups showed the method could classify COPD subjects with far greater accuracy. Models were constructed which classified subjects with ≥2% sputum eosinophilia with ROC area under the curve of 0.94 and those having frequent exacerbations 0.95. Potential biomarkers correlated to clinical variables were identified in each subgroup.ConclusionThe exhaled breath volatile organic compound profile discriminated between COPD and healthy controls and identified clinically relevant COPD subgroups. If these findings are validated in prospective cohorts, they may have diagnostic and management value in this disease.

The emergence of ambient ionization techniques and their combination with smaller, cheaper mass spectrometers is beginning to make real the possibility of mass spectrometry measurements being made routinely outside of traditional laboratory settings. Here, we describe the development of an atmospheric solids analysis probe (ASAP) source for a commercially available miniaturized, single-quadrupole mass spectrometer and subsequent modification of the instrument to allow it to run as a deployable system; we further go on to describe the application of this instrument to the identification of the contents of drug seizures. For the drug seizure analysis, a small quantity of the material (powder, tablet, resin, etc.) was dissolved in ethanol and shaken to extract the analytes, the resulting solutions were then sampled by dipping a sealed glass capillary into the solution prior to analysis by ASAP–MS. Identification of the contents of the seizures was carried out using a NIST searching approach utilizing a bespoke spectral library containing 46 compounds representative of those most commonly encountered in UK forensic laboratories. In order to increase confidence in identification the library sample and subsequent analyses were carried out using a four-channel acquisition method; each channel in this method used a different cone voltage (15, 30, 50, and 70 V) inducing differing levels of in-source fragmentation in each channel; the match score across each channel was then used for identification. Using this developed method, a set of 50 real-life drug samples was analyzed with each of these being identified correctly using the library searching method.

A proof of principle method using ion mobility-mass spectrometry (IM-MS) and collision induced dissociation (CID) coupled with micro flow ultra high-performance chromatography (UHPLC-IM-MS) has been developed to screen for steviol glycosides. Traveling wave ion mobility was used to determine rotationally averaged collision cross sections in nitrogen buffer gas (CCSN). To explore the evolving applicability of ion mobility screening, the analytical approach was initially developed and applied to the analysis of a steviol/steviol glycoside spiked chocolate spread extract. Subsequently 55 food commodities were screened using a steviol glycoside CCSN library. IM analyses produced CCSN values, enabling the unequivocal identification of the steviol glycosides and isomeric pairs (negating the reliance on product ions). In addition, coeluting isomeric species, comprising (labile fragment ions, doubly charged dimers, and multiply charged species) have been identified and resolved. Isomeric false detections were avoided, with the coeluting isomeric species quantified. A quantitative assessment of CCSN in the analysis of steviol glycosides was performed.

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