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

• Authors: Łukasz Lechowicz , Mariusz Urbaniak , Wioletta Adamus-Białek , Wiesław Kaca
• Languages of publication: 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.

Keywords

EN

infrared spectroscopy; artificial neural network; uropathogenic Escherichia coli; antibiotic resistance

• Publisher: Polish Biochemical Society
• Journal: Acta Biochimica Polonica
• Year: 2013
• Volume: 60
• Issue: 4
• Pages: 713-718

Dates

published

2013

received

2013-10-31

revised

2013-12-12

accepted

2013-12-20

Contributors

author

Łukasz Lechowicz

• Department of Microbiology, Jan Kochanowski University, Kielce, Poland

author

Mariusz Urbaniak

• Organic Chemistry Division, Jan Kochanowski University, Kielce, Poland

author

Wioletta Adamus-Białek

• Independent Department of Environmental Protection and Modeling, Jan Kochanowski University, Kielce, Poland

author

Wiesław Kaca

• Department of Microbiology, Jan Kochanowski University, Kielce, Poland

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