title: Differentiation of Listeria monocytogenes serovars by using artificial neural network analysis of Fourier-transformed infrared spectra.
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.
authors.affiliation: Abteilung Mikrobiologie, Zentralinstitut für Ernährungs- und Lebensmittelforschung, Technische Universität München, Weihenstephaner Berg 3, D-85350 Freising, Germany.
authors.forename: Cecilia A
authors.initials: CA
authors.lastname: Rebuffo-Scheer
authors.forename: Jürgen
authors.initials: J
authors.lastname: Schmitt
authors.forename: Siegfried
authors.initials: S
authors.lastname: Scherer
mesh_terms: D015373:Bacterial Typing Techniques; D005516:Food Microbiology; D006801:Humans; D008089:Listeria monocytogenes / Q000737:chemistry / Q000145:classification* / Q000166:cytology; D008088:Listeriosis / Q000382:microbiology; D016571:Neural Networks, Computer; D012015:Reference Standards; D012703:Serotyping; D017550:Spectroscopy, Fourier Transform Infrared