Rapid identification of Streptococcus and Enterococcus species using diffuse reflectance-absorbance Fourier transform infrared spectroscopy and artificial neural networks.

Diffuse reflectance-absorbance Fourier transform infrared spectroscopy (FT-IR) was used to analyse 19 hospital isolates which had been identified by conventional means to one Enterococcus faecalis, E. faecium, Streptococcus bovis, S. mitis, S. pneumoniae, or S. pyogenes. Principal components analysis of the FT-IR spectra showed that this 'unsupervised' learning method failed to form six separable clusters (one of each species) and thus could not be used to identify these bacteria base on their FT-IR spectra. By contrast, artificial neural networks (ANNs) could be trained by 'supervised' learning (using the back-propagation algorithm) with the principal components scores of derivatised spectra to recognise the strains from their FT-IR spectra. These results demonstrate that the combination of FT-IR and ANNs provides a rapid, novel and accurate bacterial identification technique.

Rapid quantitative analysis of binary mixtures of Escherichia coli strains using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks.

Pyrolysis mass spectrometry (PyMS) and multivariate calibration were used to show the high degree of relatedness between Escherichia coli HB101 and E. coli UB5201. Next, binary mixtures of these two phenotypically closely related E. coli strains were prepared and subjected to PyMS. Fully interconnected feedforward artificial neural networks (ANNs) were used to analyse the pyrolysis mass spectra to obtain quantitative information representative of level of E. coli UB5201 in E. coli HB101. The ANNs exploited were trained using the standard back propagation algorithm, and the nodes used sigmoidal squashing functions. Accurate quantitative information was obtained for mixtures with > 3% E. coli UB5201 in E. coli HB101. To remove noise from the pyrolysis mass spectra and so lower the limit of detection, the spectra were reduced using principal components analysis (PCA) and the first 13 principal components used to train ANNs. These PCA-ANNs allowed accurate estimates at levels as low as 1% E. coli UB5201 in E. coli HB101 to be predicted. In terms of bacterial numbers, it was shown that the limit of detection of PyMS in conjunction with ANNs was 3 x 10(4) E. coli UB5201 cells in 1.6 x 10(7) E. coli HB101 cells. It may be concluded that PyMS with ANNs provides a powerful and rapid method for the quantification of mixtures of closely related bacterial strains.

Differentiation of brewing yeast strains by pyrolysis mass spectrometry and Fourier transform infrared spectroscopy.

Two rapid spectroscopic approaches for whole-organism fingerprinting--pyrolysis mass spectrometry (PyMS) and Fourier transform infrared spectroscopy (FT-IR)--were used to analyse 22 production brewery Saccharomyces cerevisiae strains. Multivariate discriminant analysis of the spectral data was then performed to observe relationships between the 22 isolates. Upon visual inspection of the cluster analyses, similar differentiation of the strains was observed for both approaches. Moreover, these phenetic classifications were found to be very similar to those previously obtained using genotypic studies of the same brewing yeasts. Both spectroscopic techniques are rapid (typically 2 min for PyMS and 10 s for FT-IR) and were shown to be capable of the successful discrimination of both ale and lager yeasts. We believe that these whole-organism fingerprinting methods could find application in brewery quality control laboratories.

Rapid differentiation of closely related Candida species and strains by pyrolysis-mass spectrometry and Fourier transform-infrared spectroscopy.

Two rapid spectroscopic approaches for whole-organism fingerprinting of pyrolysis-mass spectrometry (PyMS) and Fourier transform-infrared spectroscopy (FT-IR) were used to analyze a group of 29 clinical and reference Candida isolates. These strains had been identified by conventional means as belonging to one of the three species Candida albicans, C. dubliniensis (previously reported as atypical C. albicans), and C. stellatoidea (which is also closely related to C. albicans). To observe the relationships of the 29 isolates as judged by PyMS and FT-IR, the spectral data were clustered by discriminant analysis. On visual inspection of the cluster analyses from both methods, three distinct clusters, which were discrete for each of the Candida species, could be seen. Moreover, these phenetic classifications were found to be very similar to those obtained by genotypic studies which examined the HinfI restriction enzyme digestion patterns of genomic DNA and by use of the 27A C. albicans-specific probe. Both spectroscopic techniques are rapid (typically, 2 min for PyMS and 10 s for FT-IR) and were shown to be capable of successfully discriminating between closely related isolates of C. albicans, C. dubliniensis, and C. stellatoidea. We believe that these whole-organism fingerprinting methods could provide opportunities for automation in clinical microbial laboratories, improving turnaround times and the use of resources.

Fourier transform infrared spectroscopy of follicular fluids from large and small antral follicles.

Fourier transform infrared spectroscopy (FTIR) was used to obtain 'biochemical fingerprints' for the constitution of follicular fluids from large and small antral luteinized follicles (n = 54 pairs). All samples gave reproducible characteristic biological infrared absorption spectra, with recognizable amide I protein vibrations and acyl vibrations from fatty acids. Discriminant function analysis of the first derivative FTIR spectra, together with hierarchical cluster analysis used to construct a dendrogram, showed fluid from large follicles formed a homogeneous closely related cluster, whilst that from small follicles was distinct from the large, and heterogeneous in nature. The large follicle fluids showed closer biochemical similarity to each other than to the corresponding fluid taken from small matched follicles. An artificial neural network was trained and following validation with an independent test set, successfully distinguished follicular fluids from large and small follicles. The sex steroid concentrations in the fluids from large and small follicles were significantly different. These results show that fluid from large follicles is distinct in biochemical nature from that from small follicles, but the degree of homogeneity implies size-specific changes take place. These may have consequences for the developmental potential of the oocyte.

Detection of the dipicolinic acid biomarker in Bacillus spores using Curie-point pyrolysis mass spectrometry and Fourier transform infrared spectroscopy.

Thirty-six strains of aerobic endospore-forming bacteria confirmed by polyphasic taxonomic methods to belong to Bacillus amyloliquefaciens, Bacillus cereus, Bacillus licheniformis, Bacillus megaterium, Bacillus subtilis (including Bacillus niger and Bacillus globigii), Bacillus sphaericus, and Brevi laterosporus were grown axenically on nutrient agar, and vegetative and sporulated biomasses were analyzed by Curie-point pyrolysis mass spectrometry (PyMS) and diffuse reflectance-absorbance Fourier-transform infrared spectroscopy (FT-IR). Chemometric methods based on rule induction and genetic programming were used to determine the physiological state (vegetative cells or spores) correctly, and these methods produced mathematical rules which could be simply interpreted in biochemical terms. For PyMS it was found that m/z 105 was characteristic and is a pyridine ketonium ion (C6H3ON+) obtained from the pyrolysis of dipicolinic acid (pyridine-2,6-dicarboxylic acid; DPA), a substance found in spores but not in vegetative cells; this was confirmed using pyrolysis-gas chromatography/mass spectrometry. In addition, a pyridine ring vibration at 1447-1439 cm-1 from DPA was found to be highly characteristic of spores in FT-IR analysis. Thus, although the original data sets recorded hundreds of spectral variables from whole cells simultaneously, a simple biomarker can be used for the rapid and unequivocal detection of spores of these organisms.

Rapid analysis of high-dimensional bioprocesses using multivariate spectroscopies and advanced chemometrics.

There are an increasing number of instrumental methods for obtaining data from biochemical processes, many of which now provide information on many (indeed many hundreds) of variables simultaneously. The wealth of data that these methods provide, however, is useless without the means to extract the required information. As instruments advance, and the quantity of data produced increases, the fields of bioinformatics and chemometrics have consequently grown greatly in importance. The chemometric methods nowadays available are both powerful and dangerous, and there are many issues to be considered when using statistical analyses on data for which there are numerous measurements (which often exceed the number of samples). It is not difficult to carry out statistical analysis on multivariate data in such a way that the results appear much more impressive than they really are. The authors present some of the methods that we have developed and exploited in Aberystwyth for gathering highly multivariate data from bioprocesses, and some techniques of sound multivariate statistical analyses (and of related methods based on neural and evolutionary computing) which can ensure that the results will stand up to the most rigorous scrutiny.