RAPID IDENTIFICATION OF MYCOLIC ACID PATTERNS OF MYCOBACTERIA BY HIGH-PERFORMANCE LIQUID-CHROMATOGRAPHY USING PATTERN-RECOGNITION SOFTWARE AND A MYCOBACTERIUM LIBRARY

Current methods for identifying mycobacteria by high-performance liqui d chromatography (HPLC) require a visual assessment of the generated c hromatographic data, which often involves time-consuming hand calculat ions and the use of flow charts. Our laboratory has developed a person al computer-based file containing patterns of mycolic acids detected i n 45 species of Mycobacterium, including both slowly and rapidly growi ng species, as well as Tsukamurella paurometabolum and members of the genera Corynebacterium, Nocardia, Rhodococcus, and Gordona. The librar y was designed to be used in conjunction with a commercially available pattern recognition software package, Pirouette (Infometrix, Seattle, Wash.). Pirouette uses the K-nearest neighbor algorithm, a similarity -based classification method, to categorize unknown samples on the bas is of their multivariate proximities to samples of a preassigned categ ory. Multivariate proximity is calculated from peak height data, while peak heights are named by retention time matching. The system was tes ted for accuracy by using 24 species of Mycobacterium. Of the 1,333 st rains evaluated, greater than or equal to 97% were correctly identifie d. Identification of M tuberculosis (n = 649) was 99.85% accurate, and identification of the M. avium complex (n = 211) was greater than or equal to 98% accurate; greater than or equal to 95% of strains of both double-cluster and single-cluster M. gordonae (n = 47) were correctly identified. This system provides a rapid, highly reliable assessment of HPLC-generated chromatographic data for the identification of mycob acteria.