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Updated: Sep 5, 2026

Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS
Published on: July 11, 2016
Identification of Bacteria and Viruses by Pyrolysis-Gas Chromatography-Ion Mobility Spectrometry
Daniel Röckrath1, Moritz Hitzemann1, Alejandra Vargas Valderrama2,3
1Institute of Electrical Engineering and Measurement Technology, Leibniz University Hannover, Appelstr. 9A, 30167Hannover, Germany.
Abstract:
Rapid and reliable detection of biological agents is crucial in both military and civilian contexts. Here, we present a promising field-deployable approach that combines a simple, self-built pyrolyzer (Py) with a gas chromatograph (GC) and an ultra-fast polarity switching ion mobility spectrometer (IMS). The Py-GC-IMS provides a sensitive method for detecting viruses and bacterial pathogens in liquid and solid samples. During pyrolysis, the sample is rapidly heated under an inert gas atmosphere in the absence of oxygen, leading to a transition of the sample from its nonvolatile solid or liquid state to the gas phase by breaking up the sample into its volatile constituents. Pyrolysis is followed by gas chromatography, separating the volatiles in a first dimension (retention time). For highly sensitive detection of the volatiles eluting from the GC, an ultra-fast polarity-switching IMS is used that also provides a second dimension of separation (drift time). To demonstrate the potential of this hyphenated device, five viruses and five bacteria, including two strains of the same bacterium, and simulants of biological warfare agents, were analyzed. Additionally, an unsupervised machine learning approach was applied to a representative subset of the available classes to showcase the potential of combining the device with state-of-the-art machine learning methods. So far, identification of bacteria at concentrations in the range of 108 colony-forming units per microliter (cfu/μL) and viruses at concentrations in the range of 106 viral particles/μL and lower is possible either by the GC-IMS fingerprints or by the unsupervised machine learning methods used.
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