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Rapid Identification and Classification of Listeria spp. and Serotype Assignment of Listeria monocytogenes Using Fourier Transform-Infrared Spectroscopy and Artificial Neural Network Analysis

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  • K F Romanolo
  • L Gorski
  • S Wang
  • C R Lauzon

Abstract

The use of Fourier Transform-Infrared Spectroscopy (FT-IR) in conjunction with Artificial Neural Network software NeuroDeveloper™ was examined for the rapid identification and classification of Listeria species and serotyping of Listeria monocytogenes. A spectral library was created for 245 strains of Listeria spp. to give a biochemical fingerprint from which identification of unknown samples were made. This technology was able to accurately distinguish the Listeria species with 99.03% accuracy. Eleven serotypes of Listeria monocytogenes including 1/2a, 1/2b, and 4b were identified with 96.58% accuracy. In addition, motile and non-motile forms of Listeria were used to create a more robust model for identification. FT-IR coupled with NeuroDeveloper™ appear to be a more accurate and economic choice for rapid identification of pathogenic Listeria spp. than current methods.

Suggested Citation

  • K F Romanolo & L Gorski & S Wang & C R Lauzon, 2015. "Rapid Identification and Classification of Listeria spp. and Serotype Assignment of Listeria monocytogenes Using Fourier Transform-Infrared Spectroscopy and Artificial Neural Network Analysis," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-8, November.
    • Handle: RePEc:plo:pone00:0143425
    DOI: 10.1371/journal.pone.0143425
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    Cited by:

    1. Maliha Sarfraz & Yasmin Ashraf & Samina Ashraf, 2017. "A Review: Prevalence and antimicrobial susceptibility profile of listeria species in milk products," Matrix Science Medica (MSM), Zibeline International Publishing, vol. 1(1), pages 3-9, February.

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