Classification of lactic acid bacteria with UV-resonance Raman spectroscopy

Authors

  • K. Gaus,

    1. Institut für Physikalische Chemie, Friedrich-Schiller-Universität Jena, Helmholtzweg 4, 07743 Jena, Germany
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  • P. Rösch,

    1. Institut für Physikalische Chemie, Friedrich-Schiller-Universität Jena, Helmholtzweg 4, 07743 Jena, Germany
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  • R. Petry,

    1. Institut für Physikalische Chemie, Friedrich-Schiller-Universität Jena, Helmholtzweg 4, 07743 Jena, Germany
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  • K.-D. Peschke,

    1. Lehrstuhl für Mustererkennung und, Bildverarbeitung, Institut für Informatik, Albert-Ludwigs-Universität Freiburg, Georges-Koehler-Allee Geb. 052, 79110 Freiburg, Germany
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  • O. Ronneberger,

    1. Lehrstuhl für Mustererkennung und, Bildverarbeitung, Institut für Informatik, Albert-Ludwigs-Universität Freiburg, Georges-Koehler-Allee Geb. 052, 79110 Freiburg, Germany
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  • H. Burkhardt,

    1. Lehrstuhl für Mustererkennung und, Bildverarbeitung, Institut für Informatik, Albert-Ludwigs-Universität Freiburg, Georges-Koehler-Allee Geb. 052, 79110 Freiburg, Germany
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  • K. Baumann,

    1. Institut für Pharmazie und Lebensmittelchemie, Bayerische Julius-Maximilians-Universität Würzburg, Am Hubland, 97074 Würzburg, Germany
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  • J. Popp

    Corresponding author
    1. Institut für Physikalische Chemie, Friedrich-Schiller-Universität Jena, Helmholtzweg 4, 07743 Jena, Germany
    • Institut für Physikalische Chemie, Friedrich-Schiller-Universität Jena, Helmholtzweg 4, 07743 Jena, Germany
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Abstract

UV-resonance Raman spectroscopy is applied as a method for the identification of lactic acid bacteria from yogurt. Eight different strains of bacteria from Lactobacillus acidophilus, L. delbrueckii ssp. bulgaricus, and Streptococcus thermophilus were investigated. At an excitation wavelength of 244 nm signals from nucleic acids and proteins are selectively enhanced. Classification was accomplished using different chemometric methods. In a first attempt, the unsupervised methods hierarchical cluster analysis and principal component analysis were applied to investigate natural grouping in the data. In a second step the spectra were analyzed using several supervised methods: K-nearest neighbor classifier, nearest mean classifier, linear discriminant analysis, and support vector machines. © 2006 Wiley Periodicals, Inc. Biopolymers 82: 286–290, 2006

This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com

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