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2013 | 60 | 4 | 713-718
Article title

The use of infrared spectroscopy and artificial neural networks for detection of uropathogenic Escherichia coli strains' susceptibility to cephalothin

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EN
Abstracts
EN
Background & Aims: Infrared spectroscopy is an increasingly common method for bacterial strains' testing. For the analysis of bacterial IR spectra, advanced mathematical methods such as artificial neural networks must be used. The combination of these two methods has been used previously to analyze taxonomic affiliation of bacteria. The aim of this study was the classification of Escherichia coli strains in terms of susceptibility/resistance to cephalothin on the basis of their infrared spectra. The infrared spectra of 109 uropathogenic E. coli strains were measured. These data are used for classification of E. coli strains by using designed artificial neural networks. Results: The most efficient artificial neural networks classify the E. coli sensitive/resistant strains with an error of 5%. Conclusions: Bacteria can be classified in terms of their antibiotic susceptibility by using infrared spectroscopy and artificial neural networks.
Publisher

Year
Volume
60
Issue
4
Pages
713-718
Physical description
Dates
published
2013
received
2013-10-31
revised
2013-12-12
accepted
2013-12-20
Contributors
  • Department of Microbiology, Jan Kochanowski University, Kielce, Poland
  • Organic Chemistry Division, Jan Kochanowski University, Kielce, Poland
  • Independent Department of Environmental Protection and Modeling, Jan Kochanowski University, Kielce, Poland
author
  • Department of Microbiology, Jan Kochanowski University, Kielce, Poland
References
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  • Adamus-Bialek W, Zajac E, Parniewski P, Kaca W (2013) Comparison of antibiotic resistance patterns in collections of Escherichia coli and Proteus mirabilis uropathogenic strains. Mol Biol Rep 40: 3429-3435.
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Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.bwnjournal-article-abpv60p713kz
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