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Uncovering hidden compositional changes in breath profiles using untargeted chemometric workflows


Volatile organic compounds (VOCs) emitted in breath have great potential for use in non-invasive disease diagnosis. This is largely due to the discovery of so-called ‘biomarkers’, which provide indicators of normal or abnormal states.

In large-scale clinical trials, hundreds of samples may be collected across multiple sites (e.g. clinics or hospitals) over the course of many weeks. During this biomarker discovery phase, an incorrect identification can compromise the validity of an entire trial, meaning that both robust analytical techniques and confident data mining are required.

Thermal desorption (TD) coupled with GC–MS is known as the ‘gold standard’ for breath analysis, due to its ability to capture a complete breath profile with high sensitivity. Here, we combine TD with advanced separation and detection by GC×GC–TOF MS to gain greater insight into sample composition.

However, data acquisition is just the beginning – the information-rich chromatograms must then be transformed into meaningful results. Here, we demonstrate the use of ChromCompare+, a powerful data mining and chemometrics platform, to automatically find significant differences between sample classes in complex datasets.

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