Detection of Paratuberculosis in Dairy Herds by Analyzing the Scent of Feces, Alveolar Gas, and Stable Air

• Main Authors: Michael Weber, Peter Gierschner, Anne Klassen, Elisa Kasbohm, Jochen K. Schubert, Wolfram Miekisch, Petra Reinhold, Heike Köhler
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Molecules
• Subjects: classification models; dairy cows; exhaled breath; fecal headspace; <i>Mycobacterium avium</i> ssp. <i>paratuberculosis</i> (MAP); paratuberculosis
• Online Access: https://www.mdpi.com/1420-3049/26/10/2854

Paratuberculosis is an important disease of ruminants caused by <i>Mycobacterium avium</i> ssp. <i>paratuberculosis</i> (MAP). Early detection is crucial for successful infection control, but available diagnostic tests are still dissatisfying. Methods allowing a rapid, economic, and reliable identification of animals or herds affected by MAP are urgently required. This explorative study evaluated the potential of volatile organic compounds (VOCs) to discriminate between cattle with and without MAP infections. Headspaces above fecal samples and alveolar fractions of exhaled breath of 77 cows from eight farms with defined MAP status were analyzed in addition to stable air samples. VOCs were identified by GC–MS and quantified against reference substances. To discriminate MAP-positive from MAP-negative samples, VOC feature selection and random forest classification were performed. Classification models, generated for each biological specimen, were evaluated using repeated cross-validation. The robustness of the results was tested by predicting samples of two different sampling days. For MAP classification, the different biological matrices emitted diagnostically relevant VOCs of a unique but partly overlapping pattern (fecal headspace: 19, alveolar gas: 11, stable air: 4–5). Chemically, relevant compounds belonged to hydrocarbons, ketones, alcohols, furans, and aldehydes. Comparing the different biological specimens, VOC analysis in fecal headspace proved to be most reproducible, discriminatory, and highly predictive.