ArticleFEMS microbiology letters1996
Rapid identification of Streptococcus and Enterococcus species using diffuse reflectance-absorbance Fourier transform infrared spectroscopy and artificial neural networks.
Article in FEMS microbiology letters, 1996. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Article
- Isolation and Characterization of Probiotic Bacteria from Traditional Foods.Applied biochemistry and biotechnology · 2025Article
- Selecting Milk Spectra to Develop Equations to Predict Milk Technological Traits.Foods (Basel, Switzerland) · 2021Article
- Measurement Techniques to Resolve and Control Population Dynamics of Mixed-Culture Processes.Trends in biotechnology · 2021Review
- The application of artificial neural networks in metabolomics: a historical perspective.Metabolomics : Official journal of the Metabolomic Society · 2019Review
- Spectroscopic Characterization of Bovine, Avian and Johnin Purified Protein Derivative (PPD) with High-Throughput Fourier Transform InfraRed-Based Method.Pathogens (Basel, Switzerland) · 2019Article
- Commentary on "Rapid identification of Streptococcus and Enterococcus species using diffuse reflectance-absorbance Fourier transform infrared spectroscopy and artificial neural networks".FEMS microbiology letters · 2017Article
- Understanding the antimicrobial activity of selected disinfectants against methicillin-resistant Staphylococcus aureus (MRSA).PloS one · 2017Article
- Metabolic responses of eukaryotic microalgae to environmental stress limit the ability of FT-IR spectroscopy for species identification.Algal research · 2015Article
- Implementation of Fourier transform infrared spectroscopy for the rapid typing of uropathogenic Escherichia coli.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2014Article
- Differentiation of Listeria monocytogenes serovars by using artificial neural network analysis of Fourier-transformed infrared spectra.Applied and environmental microbiology · 2007Article
- Reliable and rapid identification of Listeria monocytogenes and Listeria species by artificial neural network-based Fourier transform infrared spectroscopy.Applied and environmental microbiology · 2006Article
- Identification of oxalotrophic bacteria by neural network analysis of numerical phenetic data.Folia microbiologica · 2006Article
- Discrimination of enterobacterial repetitive intergenic consensus PCR types of Campylobacter coli and Campylobacter jejuni by Fourier transform infrared spectroscopy.Applied and environmental microbiology · 2005Article
- High-throughput metabolic fingerprinting of legume silage fermentations via Fourier transform infrared spectroscopy and chemometrics.Applied and environmental microbiology · 2004Article
- Rapid identification of Candida species by confocal Raman microspectroscopy.Journal of clinical microbiology · 2002Article
- Identification of Enterococcus, Streptococcus, and Staphylococcus by multivariate analysis of proton magnetic resonance spectroscopic data from plate cultures.Journal of clinical microbiology · 2001Article
- Classification and identification of enterococci: a comparative phenotypic, genotypic, and vibrational spectroscopic study.Journal of clinical microbiology · 2001Article
- Investigating microbial (micro)colony heterogeneity by vibrational spectroscopy.Applied and environmental microbiology · 2001Article
- Quantitative determination of the biodegradable polymer Poly(beta-hydroxybutyrate) in a recombinant Escherichia coli strain by use of mid-infrared spectroscopy and multivariative statistics.Applied and environmental microbiology · 2000Article
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5 authors.
Funding
Abstract
Diffuse reflectance-absorbance Fourier transform infrared spectroscopy (FT-IR) was used to analyse 19 hospital isolates which had been identified by conventional means to one Enterococcus faecalis, E. faecium, Streptococcus bovis, S. mitis, S. pneumoniae, or S. pyogenes. Principal components analysis of the FT-IR spectra showed that this 'unsupervised' learning method failed to form six separable clusters (one of each species) and thus could not be used to identify these bacteria base on their FT-IR spectra. By contrast, artificial neural networks (ANNs) could be trained by 'supervised' learning (using the back-propagation algorithm) with the principal components scores of derivatised spectra to recognise the strains from their FT-IR spectra. These results demonstrate that the combination of FT-IR and ANNs provides a rapid, novel and accurate bacterial identification technique.
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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.