| Autor: |
K F Romanolo, L Gorski, S Wang, C R Lauzon |
| Jazyk: |
angličtina |
| Rok vydání: |
2015 |
| Předmět: |
|
| Zdroj: |
PLoS ONE, Vol 10, Iss 11, p e0143425 (2015) |
| Druh dokumentu: |
article |
| ISSN: |
1932-6203 |
| DOI: |
10.1371/journal.pone.0143425 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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