Rapid identification of species within the Mycobacterium tuberculosis complex by artificial neural network analysis of pyrolysis mass spectra.

ABSTRACT

An artificial neural network (ANN) was trained to distinguish between Mycobacterium tuberculosis and M. bovis with averaged pyrolysis mass spectra from duplicate subcultures of four strains of each of these species, each pyrolysed in triplicate. Once trained, the ANN was interrogated with spectrum data from the original organisms (the "training set") and from 26 other mycobacterial isolates (the "challenge set") of the M. tuberculosis complex (MTBC). Eight strains of M. bovis and 13 of M. tuberculosis, whether sensitive or variously resistant to antituberculosis drugs, were identified in agreement with conventional identification. Four strains of "M. africanum" were identified as M. bovis. Of two atypical M. tuberculosis strains from South India, one was identified as M. tuberculosis and the other as M. bovis. Six strains of BCG proved heterogeneous; two gave equivocal identifications, three were identified as M. bovis and one was identified as M. tuberculosis.