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

K F Romanolo, L Gorski, S Wang and C R Lauzon

PLOS ONE, 2015, vol. 10, issue 11, 1-8

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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0143425

DOI: 10.1371/journal.pone.0143425

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