# Identification of anaerobic bacterial strains by pyrolysis-gas chromatography-ion mobility spectrometry

**Authors:** Tim Kobelt, Jonas Klose, Rumjhum Mukherjee, Martin Lippmann, Szymon P. Szafranski, Meike Stiesch, Stefan Zimmermann

PMC · DOI: 10.3389/fbioe.2025.1582565 · Frontiers in Bioengineering and Biotechnology · 2025-05-30

## TL;DR

This paper introduces a new system using pyrolysis-gas chromatography-ion mobility spectrometry to rapidly identify anaerobic bacteria with high accuracy.

## Contribution

A novel system combining pyrolysis, gas chromatography, and ion mobility spectrometry for rapid anaerobic bacterial identification is introduced.

## Key findings

- A database of fingerprints for eleven anaerobic bacterial strains was successfully created.
- Pattern recognition algorithms achieved up to 97% precision in predicting bacterial genus.

## Abstract

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%.

## Full text

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## Figures

8 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12163022/full.md

## References

41 references — full list in the complete paper: https://tomesphere.com/paper/PMC12163022/full.md

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Source: https://tomesphere.com/paper/PMC12163022