Computerized procedures for the classification and discrimination of yeasts are presented which base on feature data files and classification techniques involved in a data bank project (BD1). Microbiological (physiological and morphological) and analytical feature data classes (fatty acid patterns from gas chromatography, Fourier coefficients from FTIR spectra, fluorescence patterns) were used to show the possibilities of yeast classification, carried out by means of these features and statistical or non-statistical methods. Results obtained by a suitable combination of these methods are presented. 1ntroduct.ionThe characterization and identification of microorganisms gains an increasing interest in microbiology and biotechnology, e.g., for the selection of production strains, for the control of strain stability, for the identification of known strains and for the classification of unknown strains. Usually, diagnostic methods include the investigation of morphological, physiological and biochemical features. However, for positive identification, large test series are required. This fact makes the application of conventional methods more difficult, time consuming and inefficient. The application of analytical methods such as chromatography, optical spectroscopy and mass spectrometry has been proved useful for the characterization of microorganisms because of the fast availability of data, good reproducibility of measurements and the possibility of computer-aided data acquisition and processing. The aim of the paper is to present the possibilities of a combined application of several microbiological and analytical methods and different mathematical procedures to the classification of microbiological and biotechnological objects, mainly related to microorganisms as the most important analytical objects in biotechnology [ 13. Particularly, aspects of yeast classification are considered. To improve data handling by computational methods, the data bank project BD1 was developed which includes software for data processing by means of statistical and non-statistical procedures [Z].
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