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ARS Home » Pacific West Area » Albany, California » Western Regional Research Center » Produce Safety and Microbiology Research » Research » Publications at this Location » Publication #320098
Title: Rapid identification and classification of Listeria spp. and serotype assignment of Listeria monocytogenes using fourier transform-infrared spectroscopy and artificial neural network analysis

Author
item Romanolo, Kelly
item Gorski, Lisa
item WANG, SHUN-LIN - Brucker Daltronics
item LAUZON, CAROLR - California State University

Submitted to: Public Library of Science for Pathogens
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/4/2015
Publication Date: 11/23/2015
Citation: Romanolo, K.F., Gorski, L.A., Wang, S., Lauzon, C. 2015. Rapid identification and classification of Listeria spp. and serotype assignment of Listeria monocytogenes using fourier transform-infrared spectroscopy and artificial neural network analysis. Public Library of Science for Pathogens. PLoS ONE 10(11): e0143425. doi:10.1371/journal.pone.014325.

Interpretive Summary: We used a technology called Fourier Transform-Infrared Spectroscopy (FT-IR) to see if it could differentiate species of the bacterium Listeria. Further we used the technology to attempt to subtype strains of the foodborne pathogen L. monocytogenes. While some have shown this technology as useful in differentiating species of bacteria, it’s use as an identification and subtyping tool has been limited due to speed of use. New instruments with micro-plate readers and attached Artificial Neural Network software allow for the creation of databases with known strains to define species and subtypes of bacteria. We used 245 strains of Listeria spp. to create two databases to create an FT-IR fingerprint of the bacterial cells in order to identify unknown samples. This technology was able to accurately distinguish between 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 species than current methods.

Technical 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 between 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 species than current methods.