Volatile atmospheric pressure chemical ionisation mass spectrometry headspace analysis of E. coli and S. aureus.

Identifying the characteristics of bacterial species can improve treatment outcomes and mass spectrometry methods have been shown to be capable of identifying biomarkers of bacterial species. This study is the first to use volatile atmospheric pressure chemical ionisation mass spectrometry to directly and non-invasively analyse the headspace of E. coli and S. aureus bacterial cultures, enabling major biological classification at species level (Gram negative/positive respectively). Four different protocols were used to collect data, three utilising discrete 5 min samples taken between 2 and 96 h after inoculation and one method employing 24 h continuous sampling. Characteristic marker ions were found for both E. coli and S. aureus. A model to distinguish between sample types was able to correctly identify the bacteria samples after sufficient growth (24-48 h), with similar results obtained across different sampling methods. This demonstrates that this is a robust method to analyse and classify bacterial cultures accurately and within a relevant time frame, offering a promising technique for both clinical and research applications.

Breath selection methods for compact mass spectrometry breath analysis.

Compact mass spectrometry (CMS) is a versatile and transportable analytical instrument that has the potential to be used in clinical settings to quickly and non-invasively detect a wide range of relevant conditions from breath samples. The purpose of this study is to optimise data preprocessing protocols by three proposed methods of breath sampling, using the CMS. It also lays out a general framework for which data processing methods can be evaluated. METHODS: This paper considers data from three previous studies, each using a different breath sampling method. These include a peppermint washout study using continuous breath sampling with a purified air source, an exercise study using continuous breath sampling with an ambient air source, and a single breath sampling study with an ambient air source. For each dataset, different breath selection (data preprocessing) methods were compared and benchmarked according to predictive performance on a validation set and quantitative reliability of m/z bin intensity measurements. RESULTS: For both continuous methods, the best breath selection method improved the predictive model compared to no preselection, as measured by the 95% CI range for Youden's index, from 0.68-0.86 to 0.86-0.97 for the exercise study and 0.69-0.82 to 1.00-1.00 for the peppermint study. The reliability of intensity measurements for both datasets (as measured by median relative standard deviation (RSD)), was improved slightly by the best selection method compared to no preselection, from 18% to 14% for the exercise study and 7%-5% for the peppermint study. For the single breath samples, all the models resulted in perfect prediction, with a 95% CI range for Youden's index of 1.00-1.00. The reliability of the proposed method was 38%. CONCLUSION: The method of selecting exhaled breath from CMS data can affect the reliability of the measurement and the ability to distinguish between breath samples taken under different conditions. The application of appropriate data processing methods can improve the quality of the data and results obtained from CMS. The methods presented will enable untargeted analysis of breath VOCs using CMS to be performed.