Differentiation of Listeria Monocytogenes Serovars by Using Artificial Neural Network Analysis of Fourier-transformed Infrared Spectra

Overview

Journal Appl Environ Microbiol

Specialties Environmental Health
Microbiology

Date 2006 Dec 5

PMID 17142376

Citations 17

Authors

Cecilia A Rebuffo-Scheer

Jurgen Schmitt

Siegfried Scherer

Affiliations

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Abstract

A classification system based on Fourier transform infrared (FTIR) spectroscopy combined with artificial neural network analysis was designed to differentiate 12 serovars of Listeria monocytogenes using a reference database of 106 well-defined strains. External validation was performed using a test set of another 166 L. monocytogenes strains. The O antigens (serogroup) of 164 strains (98.8%) could be identified correctly, and H antigens were correctly determined in 152 (91.6%) of the test strains. Importantly, 40 out of 41 potentially epidemic serovar 4b strains were unambiguously identified. FTIR analysis is superior to PCR-based systems for serovar differentiation and has potential for the rapid, simultaneous identification of both species and serovar of an unknown Listeria isolate by simply measuring a whole-cell infrared spectrum.

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