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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.

plos one

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