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

Abstract

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. The most efficient artificial neural networks classify the E. coli sensitive/resistant strains with an error of 5%. Bacteria can be classified in terms of their antibiotic susceptibility by using infrared spectroscopy and artificial neural networks.