Three rapid spectroscopic approaches for whole-organism fingerprintingpyrolysis mass spectrometry (PyMS), Fourier transform infra-red spectroscopy (FT-IR) and dispersive Raman microscopy -were used to analyse a group of 59 clinical bacterial isolates associated with urinary tract infection. Direct visual analysis of these spectra was not possible, highlighting the need to use methods to reduce the dimensionality of these hyperspectral data. The unsupervised methods of discriminant function and hierarchical cluster analyses were employed to group these organisms based on their spectral fingerprints, but none produced wholly satisfactory groupings which were characteristic for each of the five bacterial types. In contrast, for PyMS and FT-IR, the artificial neural network (ANN) approaches exploiting multi-layer perceptrons or radial basis functions could be trained with representative spectra of the five bacterial groups so that isolates from clinical bacteriuria in an independent unseen test set could be correctly identified. Comparable ANNs trained with Raman spectra correctly identified some 80% of the same test set. PyMS and FT-IR have often been exploited within microbial systematics, but these are believed to be the first published data showing the ability of dispersive Raman microscopy to discriminate clinically significant intact bacterial species. These results demonstrate that modern analytical spectroscopies of high intrinsic dimensionality can provide rapid accurate microbial characterization techniques, but only when combined with appropriate chemometrics. ~~

We sought to test the hypothesis that mutant bacterial strains could be discriminated from each other on the basis of the metabolites they secrete into the medium (their ‘metabolic footprint’), using two methods of ‘global’ metabolite analysis (FT–IR and direct injection electrospray mass spectrometry). The biological system used was based on a published study of Escherichia coli tryptophan mutants that had been analysed and discriminated by Yanofsky and colleagues using transcriptome analysis. Wild-type strains supplemented with tryptophan or analogues could be discriminated from controls using FT–IR of 24 h broths, as could each of the mutant strains in both minimal and supplemented media. Direct injection electrospray mass spectrometry with unit mass resolution could also be used to discriminate the strains from each other, and had the advantage that the discrimination required the use of just two or three masses in each case. These were determined via a genetic algorithm. Both methods are rapid, reagentless, reproducible and cheap, and might beneficially be extended to the analysis of gene knockout libraries.

We describe the first application of dispersive Raman spectroscopy using a diode laser exciting at 780 nm and a charge-coupled device (CCD) detector to the noninvasive, on-line determination of the biotransformation by yeast of glucose to ethanol. Software was developed which automatically removed the effects of cosmic rays and other noise, normalized the spectra to an invariant peak, then removed the “baseline” arising from interference by fluorescent impurities, to obtain the “true” Raman spectra. Variable selection was automatically performed on the parameters of relevant Raman peaks (height, width, position of top and center, area and skewness), and a small subset used as the input to cross-validated models based on partial least-squares (PLS) regression. The multivariate calibration models so formed were sufficiently robust to be able to predict the concentration of glucose and ethanol in a completely different fermentation with a precision better than 5%. Dispersive Raman spectroscopy, when coupled with the appropriate chemometrics, is a very useful approach to the noninvasive, on-line determination of the progress of microbial fermentations.