ORIGINAL RESEARCH article

Front. Bioeng. Biotechnol.

Sec. Biosensors and Biomolecular Electronics

Volume 13 - 2025 | doi: 10.3389/fbioe.2025.1582565

This article is part of the Research TopicDevelopment of point-of-care sensors for diagnosis of bacterial-associated infectionsView all 6 articles

Identification of Anaerobic Bacterial Strains by Pyrolysis-Gas Chromatography-Ion Mobility Spectrometry

Provisionally accepted
Tim  KobeltTim Kobelt1*Jonas  KloseJonas Klose1Rumjhum  MukherjeeRumjhum Mukherjee2,3Martin  LippmannMartin Lippmann1Szymon  Piotr SzafranskiSzymon Piotr Szafranski2,3Meike  StieschMeike Stiesch2,3Stefan  ZimmermannStefan Zimmermann1
  • 1Leibniz University Hannover, Hanover, Germany
  • 2Hannover Medical School, Hanover, Lower Saxony, Germany
  • 3Lower Saxony Centre for Biomedical Engineering, Implant Research and Development (NIFE), Hannover, Germany

The final, formatted version of the article will be published soon.

The rapid identification of bacterial pathogens is critical for the early diagnosis of severe clinical conditions, such as sepsis or implant-associated infections, and for the initiation of timely, targeted therapies. This need is particularly acute within the complex oral microbiome, where diverse opportunistic pathogens contribute to a range of local and systemic diseases. While techniques such as phenotypic systems and MALDI-TOF-MS offer faster results, they remain limited by costs, and operational constraints. To address these challenges and cater to the need for rapid identification of bacteria, we present a system for identification and classification of anaerobic bacteria as a first example. This system combines a pyrolyzer, a gas chromatograph and a highly sensitive ion mobility spectrometer. The ion mobility spectrometer has been optimized for coupling with the gas chromatograph and offers simultaneously recording of ion mobility spectra in both ion polarities during one gas chromatographic separation by using two drift tubes arranged in axial configuration. Feasibility has been demonstrated by building a database of fingerprints of eleven isolated reference samples of anaerobic bacteria with clinical relevance. Preliminary experiments have demonstrated that pattern recognition algorithms can predict the genus of isolated bacteria with a precision of up to 97%.

Keywords: Biofilms, anaerobic bacteria, IDENTIFICATION, Pyrolysis, gas chromatography, ion mobility spectrometry, Support vector machine

Received: 24 Feb 2025; Accepted: 16 May 2025.

Copyright: © 2025 Kobelt, Klose, Mukherjee, Lippmann, Szafranski, Stiesch and Zimmermann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Tim Kobelt, Leibniz University Hannover, Hanover, Germany

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