Evidence map›Paper›PMID 17142376›Full record

ArticleApplied and environmental microbiology2007

Differentiation of Listeria monocytogenes serovars by using artificial neural network analysis of Fourier-transformed infrared spectra.

Cecilia A Rebuffo-Scheer, Jürgen Schmitt, Siegfried Scherer

Open access · greenAbstract readEvaluation Study
In one paragraph

Article in Applied and environmental microbiology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.1field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 79 citations in OpenAlex.

  1. Article
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  5. Decoding Optical Data with Machine Learning.Laser & photonics reviews · 2021
    Article
  6. Review
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  8. 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 · 2019
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Cecilia A Rebuffo-ScheerAbteilung Mikrobiologie, Zentralinstitut für Ernährungs- und Lebensmittelforschung, Technische Universität München, Weihenstephaner Berg 3, D-85350 Freising, Germany.
Jürgen Schmitt
Siegfried Scherer
Leibniz-Institute for Food Systems Biology at the Technical University of Munich · DETechnical University of Munich · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A classification system based on Fourier transform infrared (FTIR) spectroscopy combined with artificial neural network analysis was designed to differentiate 12 serovars of Listeria monocytogenes using a reference database of 106 well-defined strains. External validation was performed using a test set of another 166 L. monocytogenes strains. The O antigens (serogroup) of 164 strains (98.8%) could be identified correctly, and H antigens were correctly determined in 152 (91.6%) of the test strains. Importantly, 40 out of 41 potentially epidemic serovar 4b strains were unambiguously identified. FTIR analysis is superior to PCR-based systems for serovar differentiation and has potential for the rapid, simultaneous identification of both species and serovar of an unknown Listeria isolate by simply measuring a whole-cell infrared spectrum.

Indexed as

Bacterial Typing TechniquesNeural Networks, ComputerFood MicrobiologyHumansListeria monocytogenesListeriosisReference StandardsSerotypingSpectroscopy, Fourier Transform Infrared

Identifiers

PMID17142376
PMCPMC1800759
OpenAlexW2163639044

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.