Analytical and Bioanalytical Chemistry

, Volume 407, Issue 3, pp 787–794 | Cite as

Raman spectroscopic detection and identification of Burkholderia mallei and Burkholderia pseudomallei in feedstuff

  • Stephan Stöckel
  • Susann Meisel
  • Mandy Elschner
  • Falk Melzer
  • Petra Rösch
  • Jürgen Popp
Research Paper
Part of the following topical collections:
  1. ABCs 13th Anniversary


Burkholderia mallei (the etiologic agent of glanders in equines and rarely humans) and Burkholderia pseudomallei, causing melioidosis in humans and animals, are designated category B biothreat agents. The intrinsically high resistance of both agents to many antibiotics, their potential use as bioweapons, and their low infectious dose, necessitate the need for rapid and accurate detection methods. Current methods to identify these organisms may require up to 1 week, as they rely on phenotypic characteristics and an extensive set of biochemical reactions. In this study, Raman microspectroscopy, a cultivation-independent typing technique for single bacterial cells with the potential for being a rapid point-of-care analysis system, is evaluated to identify and differentiate B. mallei and B. pseudomallei within hours. Here, not only broth-cultured microbes but also bacteria isolated out of pelleted animal feedstuff were taken into account. A database of Raman spectra allowed a calculation of classification functions, which were trained to differentiate Raman spectra of not only both pathogens but also of five further Burkholderia spp. and four species of the closely related genus Pseudomonas. The developed two-stage classification system comprising two support vector machine (SVM) classifiers was then challenged by a test set of 11 samples to simulate the case of a real-world-scenario, when “unknown samples” are to be identified. In the end, all test set samples were identified correctly, even if the contained bacterial strains were not incorporated in the database before or were isolated out of animal feedstuff. Specifically, the five test samples bearing B. mallei and B. pseudomallei were correctly identified on species level with accuracies between 93.9 and 98.7 %. The sample analysis itself requires no biomass enrichment step prior to the analysis and can be performed under biosafety level 1 (BSL 1) conditions after inactivating the bacteria with formaldehyde.


Animal feedstuff Burkholderia mallei Burkholderia pseudomallei Pseudomonas Raman spectroscopy 



Funding of the research projects “Pathosafe” (FKZ 13N9547 and FKZ 13N9549) and “RamaDek” (FKZ 13N11168) from the Federal Ministry of Education and Research, Germany (BMBF) as well as funding of “EQADeBa” by the EU, EAHC Agreement no. 2007 204 is gratefully acknowledged. We also thank Katja Fischer (Friedrich Loeffler Institute, Germany) for doing the sample preparation and inactivation experiments. We highly appreciate the help of Dr. Holger Scholz, Institute of Microbiology, Federal Armed Forces, Munich, Germany, and Dr. Ulrich Wernery, Central Veterinary Research Institute, Dubai, UAE for providing B. mallei and B. pseudomallei strains.


  1. 1.
    Galyov EE, Brett PJ, DeShazer D (2010) Molecular Insights into Burkholderia pseudomallei and Burkholderia mallei pathogenesis. Annu Rev Microbiol 64:495–517CrossRefGoogle Scholar
  2. 2.
    Estes DM, Dow SW, Schweizer HP, Torres AG (2010) Present and future therapeutic strategies for melioidosis and glanders. Expert Rev Anti-Infect Ther 8(3):325–338CrossRefGoogle Scholar
  3. 3.
    Coenye T, Vandamme P (2003) Diversity and significance of Burkholderia species occupying diverse ecological niches. Environ Microbiol 5(9):719–729CrossRefGoogle Scholar
  4. 4.
    Fong IW, Alibek K (2009) Bioterrorism and infectious agents: a new dilemma for the 21st century. Emerging infectious diseases of the 21st century. Springer-Verlag, New YorkCrossRefGoogle Scholar
  5. 5.
    Wheelis M (1998) First shots fired in biological warfare. Nature 395(6699):213–213CrossRefGoogle Scholar
  6. 6.
    Cheng AC (2010) Melioidosis: advances in diagnosis and treatment. Curr Opin Infect Dis 23(6):554–559CrossRefGoogle Scholar
  7. 7.
    Khan I, Wieler LH, Melzer F, Elschner MC, Muhammad G, Ali S, Sprague LD, Neubauer H, Saqib M (2013) Glanders in animals: a review on epidemiology, clinical presentation, diagnosis and countermeasures. Transbound Emerg Dis 60(3):204–221CrossRefGoogle Scholar
  8. 8.
    Lowe W, March JK, Bunnell AJ, O’Neill KL, Robison RA (2014) PCR-based methodologies used to detect and differentiate the Burkholderia pseudomallei complex: B. pseudomallei, B. mallei, and B. thailandensis. Curr Issues Mol Biol 16(1):23–54Google Scholar
  9. 9.
    Hagen RM, Frickmann H, Elschner M, Melzer F, Neubauer H, Gauthier YP, Racz P, Poppert S (2011) Rapid identification of Burkholderia pseudomallei and Burkholderia mallei by fluorescence in situ hybridization (FISH) from culture and paraffin-embedded tissue samples. Int J Med Microbiol 301(7):585–590CrossRefGoogle Scholar
  10. 10.
    Podin Y, Kaestli M, McMahon N, Hennessy J, Ngian HU, Wong JS, Mohana A, Wong SC, William T, Mayo M, Baird RW, Currie BJ (2013) Reliability of automated biochemical identification of Burkholderia pseudomallei is regionally dependent. J Clin Microbiol 51(9):3076–3078Google Scholar
  11. 11.
    Karger A, Stock R, Ziller M, Elschner MC, Bettin B, Melzer F, Maier T, Kostrzewa M, Scholz HC, Neubauer H, Tomaso H (2012) Rapid identification of Burkholderia mallei and Burkholderia pseudomallei by intact cell matrix-assisted laser desorption/ionisation mass spectrometric typing. BMC Microbiol 12Google Scholar
  12. 12.
    Li D, March JK, Bills TM, Holt BC, Wilson CE, Lowe W, Tolley HD, Lee ML, Robison RA (2013) Gas chromatography-mass spectrometry method for rapid identification and differentiation of Burkholderia pseudomallei and Burkholderia mallei from each other, Burkholderia thailandensis and several members of the Burkholderia cepacia complex. J Appl Microbiol 115(5):1159–1171CrossRefGoogle Scholar
  13. 13.
    Stöckel S, Meisel S, Elschner M, Rösch P, Popp J (2012) Raman spectroscopic detection of anthrax endospores in powder samples. Angew Chem, Int Ed 51(22):5339–5342Google Scholar
  14. 14.
    Stöckel S, Meisel S, Elschner M, Rösch P, Popp J (2012) Identification of Bacillus anthracis via Raman spectroscopy and chemometric approaches. Anal Chem 84(22):9873–9880CrossRefGoogle Scholar
  15. 15.
    Meisel S, Stöckel S, Elschner M, Melzer F, Rösch P, Popp J (2012) Raman spectroscopy as a potential tool for detection of Brucella spp. in milk. Appl Environ Microbiol 78(16):5575–5583CrossRefGoogle Scholar
  16. 16.
    Meisel S, Stöckel S, Rösch P, Popp J (2014) Identification of meat-associated pathogens via Raman microspectroscopy. Food Microbiol 38:36–43CrossRefGoogle Scholar
  17. 17.
    Kloß S, Kampe B, Sachse S, Rösch P, Straube E, Pfister W, Kiehntopf M, Popp J (2013) Culture independent Raman spectroscopic identification of urinary tract infection pathogens: a proof of principle study. Anal Chem 85(20):9610–9616CrossRefGoogle Scholar
  18. 18.
    Stöckel S, Schumacher W, Meisel S, Elschner M, Rösch P, Popp J (2010) Raman spectroscopy compatible inactivation method for pathogenic endospores. Appl Environ Microbiol 76(9):2895–2907Google Scholar
  19. 19.
    R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  20. 20.
    Morhác M (2009) An algorithm for determination of peak regions and baseline elimination in spectroscopic data. Nucl Instrum Meth A 600(2):478–487CrossRefGoogle Scholar
  21. 21.
    Carrabba MM (2006) Wavenumber standards for Raman spectrometry. Handbook of vibrational spectroscopy, vol 1. John Wiley & Sons, Ltd., ChichesterGoogle Scholar
  22. 22.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297Google Scholar
  23. 23.
    Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167CrossRefGoogle Scholar
  24. 24.
    Desbois A (1994) Resonance Raman spectroscopy of c-type cytochromes. Biochimie 76(7):693–707CrossRefGoogle Scholar
  25. 25.
    Ciobotă V, Burkhardt EM, Schumacher W, Rösch P, Küsel K, Popp J (2011) The influence of intracellular storage material on bacterial identification by means of Raman spectroscopy. Anal Bioanal Chem 397(7):2929–2937CrossRefGoogle Scholar
  26. 26.
    Movasaghi Z, Rehman S, Rehman IU (2007) Raman spectroscopy of biological tissues. Appl Spectrosc Rev 42(5):493–541CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Stephan Stöckel
    • 1
  • Susann Meisel
    • 1
  • Mandy Elschner
    • 2
  • Falk Melzer
    • 2
  • Petra Rösch
    • 1
  • Jürgen Popp
    • 1
    • 3
  1. 1.Institute of Physical Chemistry and Abbe School of PhotonicsFriedrich Schiller University JenaJenaGermany
  2. 2.Friedrich-Loeffler-Institut, Federal Research Institute for Animal HealthInstitute of Bacterial Infections and ZoonosesJenaGermany
  3. 3.Leibniz Institute of Photonic TechnologyJenaGermany

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