ArticlePloS one2015
Rapid Identification and Classification of Listeria spp. and Serotype Assignment of Listeria monocytogenes Using Fourier Transform-Infrared Spectroscopy and Artificial Neural Network Analysis.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Fourier transform infrared spectroscopy enables rapid strain typing and cluster analysis ofFrontiers in microbiology · 2026Article
- Biochemical characterization of pathogenic bacterial species using Raman spectroscopy and discrimination model based on selected spectral features.Lasers in medical science · 2021Article
- Fourier transform infrared spectroscopy: unlocking fundamentals and prospects for bacterial strain typing.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2019Review
- MIR spectroscopy as alternative method for further confirmation of foodborne pathogensJournal of food science and technology · 2018Article
- A pilot study on fingerprinting Leishmania species from the Old World using Fourier transform infrared spectroscopy.Analytical and bioanalytical chemistry · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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