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Modelling and Classification of GC/IMS Breath Gas Measurements for Lozenges of Different Flavours

  • Claudia WigmannEmail author
  • Laura Lange
  • Wolfgang Vautz
  • Katja Ickstadt
Chapter
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

The composition of exhaled breath contains information about diet, oral hygiene and other environmental influences as well as on the state of health and on medications. Therefore, rapid and sensitive breath analysis would be a helpful tool, for example, for medical diagnosis or therapy control. Ion mobility spectrometry coupled with gas-chromatographic pre-separation (GC/IMS) meet those requirements and can be used to distinguish between healthy and diseased persons or to detect drug usage, for example, based on characteristic exhaled metabolites. So far, the detection of peaks in IMS measurements and the assignment of compounds is done manually and an automated procedure is urgently needed. In this article, we analyse breath gas measurements by GC/IMS from a volunteer having consumed lozenges of 12 different flavours. The IMS measurements are modelled along drift time with an additive model of unimodal regressions to describe each peak. The regressions are afterwards combined across all spectra and all datasets to determine typical peak locations and the respective heights of the peaks in each measurement are inferred. The obtained matrix of peak intensities is then used to classify the measurements into the 12 flavour groups using support vector machines. Since the true class labels are known, we can assess the mis-classification rate using cross-validation.

Notes

Acknowledgements

The financial support of the Bundesministerium für Bildung und Forschung and the Ministerium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen is gratefully acknowledged. This work has also been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 ‘Providing Information by Resource-Constrained Analysis’, Project C4.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Claudia Wigmann
    • 1
    Email author
  • Laura Lange
    • 2
  • Wolfgang Vautz
    • 3
    • 4
  • Katja Ickstadt
    • 5
  1. 1.IUF – Leibniz-Institut für umweltmedizinische Forschung gGmbHDüsseldorfGermany
  2. 2.OptiMedis AGHamburgGermany
  3. 3.ION-GAS GmbHDortmundGermany
  4. 4.Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V.DortmundGermany
  5. 5.Faculty of StatisticsTU Dortmund UniversityDortmundGermany

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