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  • 1995-1999  (3)
  • 1
    Electronic Resource
    Electronic Resource
    Rapid identification of Streptococcus and Enterococcus species using diffuse reflectance-absorbance Fourier transform infrared spectroscopy and artificial neural networks (1996)
    Oxford, UK : Blackwell Publishing Ltd
    FEMS microbiology letters 140 (1996), S. 0 
    Details
    ISSN: 1574-6968
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology
    Notes: Abstract 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 of 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 for each species) and thus could not be used to identify these bacteria based 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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1574-6968.1996.tb08342.x
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    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    The deconvolution of pyrolysis mass spectra using genetic programming: application to the identification of some Eubacterium species (1998)
    Oxford, UK : Blackwell Publishing Ltd
    FEMS microbiology letters 160 (1998), S. 0 
    Details
    ISSN: 1574-6968
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology
    Notes: Pyrolysis mass spectrometry was used to produce complex biochemical fingerprints of Eubacterium exiguum, E. infirmum, E. tardum and E. timidum. To examine the relationship between these organisms the spectra were clustered by canonical variates analysis, and four clusters, one for each species, were observed. In an earlier study we trained artificial neural networks to identify these clinical isolates successfully; however, the information used by the neural network was not accessible from this so-called ‘black box’ technique. To allow the deconvolution of such complex spectra (in terms of which masses were important for discrimination) it was necessary to develop a system that itself produces ‘rules’ that are readily comprehensible. We here exploit the evolutionary computational technique of genetic programming; this rapidly and automatically produced simple mathematical functions that were also able to classify organisms to each of the four bacterial groups correctly and unambiguously. Since the rules used only a very limited set of masses, from a search space some 50 orders of magnitude greater than the dimensionality actually necessary, visual discrimination of the organisms on the basis of these spectral masses alone was also then possible.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1574-6968.1998.tb12917.x
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    Paper (German National Licenses)
    Fulltext
  • 3
    Electronic Resource
    Electronic Resource
    Quantification of microbial productivity via multi-angle light scattering and supervised learning (1998)
    New York, NY [u.a.] : Wiley-Blackwell
    Biotechnology and Bioengineering 59 (1998), S. 131-143 
    Details
    ISSN: 0006-3592
    Keywords: chemometrics ; light scattering ; microbial productivity ; Chemistry ; Biochemistry and Biotechnology
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Biology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: This article describes the use of chemometric methods for prediction of biological parameters of cell suspensions on the basis of their light scattering profiles. Laser light is directed into a vial or flow cell containing media from the suspension. The intensity of the scattered light is recorded at 18 angles. Supervised learning methods are then used to calibrate a model relating the parameter of interest to the intensity values. Using such models opens up the possibility of estimating the biological properties of fermentor broths extremely rapidly (typically every 4 sec), and, using the flow cell, without user interaction. Our work has demonstrated the usefulness of this approach for estimation of yeast cell counts over a wide range of values (105-109 cells mL-1), although it was less successful in predicting cell viability in such suspensions. © 1998 John Wiley & Sons, Inc. Biotechnol Bioeng 59:131-143, 1998.
    Additional Material: 15 Ill.
    Type of Medium: Electronic Resource
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    Paper (German National Licenses)
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