IDENTIFICATION OF LACTOBACILLUS STRAINS AT THE SPECIES LEVEL USING FTIR SPECTROSCOPY AND ARTIFICIAL NEURAL NETWORKS
 
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Pol. J. Food Nutr. Sci. 2007;57(3):301–306
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ABSTRACT
Fourier-transform infrared (FTIR) spectroscopy and artificial neural networks were used to identify bacteria of the genus Lactobacillus at the species level. A previously developed method for measuring FTIR spectra, and a strategy for their analysis provided the basis for selecting the FTIR spectra of the tested bacteria, and for creating a spectral library, as described elsewhere [Dziuba et al., 2007b]. In our previous study [Dziuba et al., 2007b] we demonstrated that the FTIR spectral characteristics of Lactobacillus strains based exclusively on the differentiation index D, calculated from the Pearson’s correlation coefficient, and cluster analysis are not sufficient to describe the relationships between FTIR spectra and bacteria as molecular systems in a way that would permit their proper identification. Thus, research was launched in which the spectra collected in the above library were used for developing artificial neural networks. The practical value of these networks was verified based on the results of identification of 17 bacterial strains of known taxonomy as well as 7 strains isolated from dairy products and identified on the basis of their taxonomy and biochemical tests. The application of artificial neural networks, i.e. the most advanced chemometric method, to analysis of FTIR spectra enabled correct identification of 93% of bacterial strains of the genus Lactobacillus.
ISSN:1230-0322