A combined mid-infrared spectroscopic/statistical modeling approach for the discrimination and identification, at the strain level, of both sporulated and vegetative bacterial samples is presented. Transmission mode spectra of bacteria dried on ZnSe windows were collected using a Fourier transform mid-infrared (FT-IR) spectrometer. Five <i>Bacillus</i> bacterial strains (<i>B. atrophaeus</i> 49337, <i>B. globigii</i> Dugway, <i>B. thuringiensis</i> spp. <i>kurstaki</i> 35866, <i>B. subtilis</i> 49760, and <i>B. subtilis</i> 6051) were used to construct a reference spectral library and to parameterize a four-step statistical model for the systematic identification of bacteria. The statistical methods used in this initial feasibility study included principal component analysis (PCA), classification and regression trees (CART), and Mahalanobis distance calculations. Internal cross-validation studies successfully classified 100% of the samples into their correct physiological state (sporulated or vegetative) and identified 67% of the samples correctly as to their bacterial strain. Analysis of thirteen blind samples, which included reference and other bacteria, nonbiological materials, and mixtures of both nonbiological and bacterial samples, yielded comparable accuracy. The primary advantage of this approach is the accurate identification of unknown bacteria, including <i>spores</i>, in a matter of minutes.

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