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Current methods for identifying mycobacteria by high-performance liquid chromatography (HPLC) require a visual assessment of the generated chromatographic data, which often involves time-consuming hand calculations and the use of flow charts. Our laboratory has developed a personal computer-based file containing patterns of mycolic acids detected in 45 species of Mycobacterium, including both slowly and rapidly growing species, as well as Tsukamurella paurometabolum and members of the genera Corynebacterium, Nocardia, Rhodococcus, and Gordona. The library 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 basis of their multivariate proximities to samples of a preassigned category. Multivariate proximity is calculated from peak height data, while peak heights are named by retention time matching. The system was tested for accuracy by using 24 species of Mycobacterium. Of the 1,333 strains evaluated, &gt; or = 97% were correctly identified. Identification of M. tuberculosis (n = 649) was 99.85% accurate, and identification of the M. avium complex (n = 211) was &gt; or = 98% accurate; &gt; or = 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 mycobacteria.

journal of clinical microbiology

Rapid identification of mycolic acid patterns of mycobacteria by high-performance liquid chromatography using pattern recognition software and a Mycobacterium library