@article{BaruchaMauchDucksteinetal.2022,
  author    = {Barucha, Anton and Mauch, Renan Marrichi and Duckstein, Franziska and Zagoya, Carlos and Mainz, Jochen G.},
  title     = {The potential of volatile organic compound analysis for pathogen detection and disease monitoring in patients with cystic fibrosis},
  series = {Expert review of respiratory medicine},
  volume    = {16},
  journal   = {Expert review of respiratory medicine},
  number    = {7},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {1747-6348},
  doi       = {10.1080/17476348.2022.2104249},
  pages     = {723 -- 735},
  year      = {2022},
  abstract  = {Introduction Airway infection with pathogens and its associated pulmonary exacerbations (PEX) are the major causes of morbidity and premature death in cystic fibrosis (CF). Preventing or postponing chronic infections requires early diagnosis. However, limitations of conventional microbiology-based methods can hamper identification of exacerbations and specific pathogen detection. Analyzing volatile organic compounds (VOCs) in breath samples may be an interesting tool in this regard, as VOC-biomarkers can characterize specific airway infections in CF. Areas covered We address the current achievements in VOC-analysis and discuss studies assessing VOC-biomarkers and fingerprints, i.e. a combination of multiple VOCs, in breath samples aiming at pathogen and PEX detection in people with CF (pwCF). We aim to provide bases for further research in this interesting field. Expert opinion Overall, VOC-based analysis is a promising tool for diagnosis of infection and inflammation with potential to monitor disease progression in pwCF. Advantages over conventional diagnostic methods, including easy and non-invasive sampling procedures, may help to drive prompt, suitable therapeutic approaches in the future. Our review shall encourage further research, including validation of VOC-based methods. Specifically, longitudinal validation under standardized conditions is of interest in order to ensure repeatability and enable inclusion in CF diagnostic routine.},
  language  = {en}
}
@article{ErlerRiebeBeitzetal.2018,
  author    = {Erler, Alexander and Riebe, Daniel and Beitz, Toralf and L{\"o}hmannsr{\"o}ben, Hans-Gerd and Grothusheitkamp, Daniela and Kunz, T. and Methner, Frank-J{\"u}rgen},
  title     = {Detection of volatile organic compounds in the headspace above mold fungi by GC-soft X-radiation-based APCI-MS},
  series = {Journal of mass spectrometr},
  volume    = {53},
  journal   = {Journal of mass spectrometr},
  number    = {10},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {1076-5174},
  doi       = {10.1002/jms.4210},
  pages     = {911 -- 920},
  year      = {2018},
  abstract  = {Mold fungi on malting barley grains cause major economic loss in malting and brewery facilities. Possible proxies for their detection are volatile and semivolatile metabolites. Among those substances, characteristic marker compounds have to be identified for a confident detection of mold fungi in varying surroundings. The analytical determination is usually performed through passive sampling with solid phase microextraction, gas chromatographic separation, and detection by electron ionization mass spectrometry (EI-MS), which often does not allow a confident determination due to the absence of molecular ions. An alternative is GC-APCI-MS, generally, allowing the determination of protonated molecular ions. Commercial atmospheric pressure chemical ionization (APCI) sources are based on corona discharges, which are often unspecific due to the occurrence of several side reactions and produce complex product ion spectra. To overcome this issue, an APCI source based on soft X-radiation is used here. This source facilitates a more specific ionization by proton transfer reactions only. In the first part, the APCI source is characterized with representative volatile fungus metabolites. Depending on the proton affinity of the metabolites, the limits of detection are up to 2 orders of magnitude below those of EI-MS. In the second part, the volatile metabolites of the mold fungus species Aspergillus, Alternaria, Fusarium, and Penicillium are investigated. In total, 86 compounds were found with GC-EI/APCI-MS. The metabolites identified belong to the substance classes of alcohols, aldehydes, ketones, carboxylic acids, esters, substituted aromatic compounds, terpenes, and sesquiterpenes. In addition to substances unspecific for the individual fungus species, characteristic patterns of metabolites, allowing their confident discrimination, were found for each of the 4 fungus species. Sixty-seven of the 86 metabolites are detected by X-ray-based APCI-MS alone. The discrimination of the fungus species based on these metabolites alone was possible. Therefore, APCI-MS in combination with collision induced dissociation alone could be used as a supervision method for the detection of mold fungi.},
  language  = {en}
}