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Applied and Environmental Microbiology, February 2006, p. 994-1000, Vol. 72, No. 2
0099-2240/06/$08.00+0     doi:10.1128/AEM.72.2.994-1000.2006
Copyright © 2006, American Society for Microbiology. All Rights Reserved.

Reliable and Rapid Identification of Listeria monocytogenes and Listeria Species by Artificial Neural Network-Based Fourier Transform Infrared Spectroscopy{dagger}

Cecilia A. Rebuffo,1 Jürgen Schmitt,2 Mareike Wenning,1 Felix von Stetten,3 and Siegfried Scherer1*

Abteilung Mikrobiologie, Zentralinstitut für Ernährungs- und Lebensmittelforschung (ZIEL), Technische Universität München, D-85350 Freising, Germany,1 Synthon GmbH, Im Neuenheimer Feld 583, D-69120 Heidelberg, Germany,2 Institut für Mikrosystemtechnik, Universität Freiburg, 79110 Freiburg, Germany3

Received 18 July 2005/ Accepted 7 November 2005

Differentiation of the species within the genus Listeria is important for the food industry but only a few reliable methods are available so far. While a number of studies have used Fourier transform infrared (FTIR) spectroscopy to identify bacteria, the extraction of complex pattern information from the infrared spectra remains difficult. Here, we apply artificial neural network technology (ANN), which is an advanced multivariate data-processing method of pattern analysis, to identify Listeria infrared spectra at the species level. A hierarchical classification system based on ANN analysis for Listeria FTIR spectra was created, based on a comprehensive reference spectral database including 243 well-defined reference strains of Listeria monocytogenes, L. innocua, L. ivanovii, L. seeligeri, and L. welshimeri. In parallel, a univariate FTIR identification model was developed. To evaluate the potentials of these models, a set of 277 isolates of diverse geographical origins, but not included in the reference database, were assembled and used as an independent external validation for species discrimination. Univariate FTIR analysis allowed the correct identification of 85.2% of all strains and of 93% of the L. monocytogenes strains. ANN-based analysis enhanced differentiation success to 96% for all Listeria species, including a success rate of 99.2% for correct L. monocytogenes identification. The identity of the 277-strain test set was also determined with the standard phenotypical API Listeria system. This kit was able to identify 88% of the test isolates and 93% of L. monocytogenes strains. These results demonstrate the high reliability and strong potential of ANN-based FTIR spectrum analysis for identification of the five Listeria species under investigation. Starting from a pure culture, this technique allows the cost-efficient and rapid identification of Listeria species within 25 h and is suitable for use in a routine food microbiological laboratory.

* Corresponding author. Mailing address: Abteilung Mikrobiologie, Zentralinstitut für Ernährungs- und Lebensmittelforschung, Technische Universität München, Weihenstephaner Berg 3, D-85350 Freising, Germany. Phone: 49 8161 713516. Fax: 49 8161 714512. E-mail: siegfried.scherer{at}wzw.tum.de.

{dagger} Supplemental material for this article may be found at http://aem.asm.org/.

Applied and Environmental Microbiology, February 2006, p. 994-1000, Vol. 72, No. 2
0099-2240/06/$08.00+0     doi:10.1128/AEM.72.2.994-1000.2006
Copyright © 2006, American Society for Microbiology. All Rights Reserved.



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