Rapid Identification and Classification of Listeria spp. and Serotype Assignment of Listeria monocytogenes Using Fourier Transform-Infrared Spectroscopy and Artificial Neural Network Analysis.
Where this comes from
- Record sourced from PubMed, PMID 26600423.
- Also identified by DOI 10.1371/journal.pone.0143425 and PMC identifier 4658148.
- Licence recorded as CC0.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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 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.
Medical subject headings
- Listeria monocytogenes
- Neural Networks, Computer