Title Construction of probability identification matrix and selective medium for acidophilic actinomycetes using numerical classification data
Author Seong, Chi Nam * · Park, Seok Kyu¹ · Goodfellow, Michael² · Kim, Seung Bum³ · Hah, Yung Chil³
Address Department of Biology College of Natural Sciences, Sunchon National University; ¹Department of Food and Nutrition, College of Natural Sciences, Sunchon National University; ²The Medical School, Framlington Placem Newcastle upon Tyne NE2 4HH, UK; ³Department of Microbiology and Research Center for Molecular Microbiology, Seoul National University
Bibliography Journal of Microbiology, 33(2),95-102, 1995,
Key Words probability identification matrix, acidophilic Streptomyces, HMO, diagnostic, cluster overlap, identification score, selective medium
Abstract A probability identification matrix of acidophilic Streptomyces was constructed. The phenetic data of the strains were derived from numerical classification described by Seong et al. The minimum number of diagnostic characters was determined using computer programs for calculation of different separation indices. The resulting matrix consisted of 25 clusters versus 53 characters. Theoretical evaluation of this matrix was achieved by estimating the cluster overlap and the identification scores for the Hypothetical Median Organisms (HMO) and for the representatives of each cluster. Cluster overlap was found to be relatively small. Identification scores for the HMO and the randomly selected representatives of each cluster were satisfactory. The matrix was assessed practically by applying the matrix to the identification of unknown isolates. Of the unknown isolates, 71.9% were clearly identified to one of eight clusters. The numerical classification data was also used to design a selective isolation medium for antibiotic-producing organisms. Four chemical substances including 2 antibiotics were determined by the DLACHAR program as diagnostic for the isolation of target organisms which have antimicrobial activity against Micrococcus luteus. It was possible to detect the increased rate of selective isolation on the synthesized medium. The results show that the numerical phenetic data can be applied to a variety of purposes, such as construction of identification matrix and selective isolation medium for acidophilic antinomycetes.
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