Elucidation of high-level chemical mechanisms using GCxGC-MS (Caroline Gauchotte-Lindsay, MDCW 2023)

Mo, 5.2.2024
| Original article from: The Multidimensional Chromatography (MDC) Workshop
We aim to simplify complex data to elucidate high-level mechanisms of engineering processes using non-targeted (NT) analysis by two-dimensional gas chromatography coupled with mass spectrometry (GCxGC-MS).
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  • Photo: MDCW: Elucidation of high-level chemical mechanisms using GCxGC-MS (Caroline Gauchotte-Lindsay, MDCW 2023)
  • Video: LabRulez: Caroline Gauchotte-Lindsay: Elucidation of high-level chemical mechanisms using GCxGC-MS (MDCW 2023)

🎤 Presenter: Caroline Gauchotte-Lindsay¹, Laurie Savage¹, Jeanine Lenselink¹, Ioannis Sampsonidis¹´² (¹James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom. ²Department of Nutritional Sciences, International Hellenic University, Thessaloniki, Greece)

Abstract

We aim to simplify complex data to elucidate high-level mechanisms of engineering processes using non-targeted (NT) analysis by two-dimensional gas chromatography coupled with mass spectrometry (GCxGC-MS). We present here sample and data pipelines to describe chemical transformations of by-products during the underground gasification of coal. By using NT analysis, chemical signatures and chemical markers of the reactions can be identified and related to the reaction conditions and parameters.

We focus here on the formation of semi-volatile organic compounds (SVOCs) during underground coal gasification (UCG) from two different experimental set-ups. Coal tar samples were collected from a shallow in situ UCG experiment and processed (or waste) waters from two ex situ experiments. The sample workflows include, for each type of sample, novel ultra-sound assisted exhaustive extraction and an adapted GCxGC chromatographic method. The data pipeline comprises tile-based alignment, Fisher ratio selection of features, sample normalisation and clustering of compounds rather than samples using affinity propagation. Affinity propagation is a clustering algorithm based on “message passing” between data pointspoint. The clusters grouped together compounds that have the same reaction kinetics patterns. It enabled us to better describe the coal gasification processes compared to the quantitative analysis of targeted compounds and to identify markers or exemplars of the reactions that could be used in future experiments to monitor progress and yield.

The Multidimensional Chromatography (MDC) Workshop
 

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