Lukasz Lechowicz1, Mariusz Urbaniak2, Wioletta Adamus-Białek3, Wiesław Kaca1. 1. Department of Microbiology, Jan Kochanowski University, Kielce, Poland. 2. Organic Chemistry Division, Jan Kochanowski University, Kielce, Poland. 3. Independent Department of Environmental Protection and Modeling, Jan Kochanowski University, Kielce, Poland.
Abstract
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.
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.