Reading research in Great Britain — 1981

Goodacre, Elizabeth J.

doi:10.1111/j.1467-9345.1982.tb00698.x

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…An alternative approach in microbial characterization is the use of spectroscopic methods. Pyrolysis mass spectrometry has been evaluated as a method for bacterial characterization; − however, thus far the high instrument costs have hindered the widespread use of this method . The use of Fourier transform infrared (FT-IR) spectroscopy for microbial identification and characterization has been gaining acceptance since Naumann and co-workers published their pioneering work in this field. − Manfait et al − have used FT-IR spectroscopy to identify drug resistance in bacteria, indicating the high information content of this technique.…”

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confidence: 99%

Raman Spectroscopic Method for Identification of Clinically Relevant Microorganisms Growing on Solid Culture Medium

et al. 1999

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Routine clinical microbiological identification of pathogenic microorganisms is largely based on nutritional and biochemical tests. In the case of severely ill patients, the unavoidable time delay associated with such identification procedures can be fatal. We present a novel identification method based on confocal Raman microspectroscopy. With this approach it is possible to obtain Raman spectra directly from microbial microcolonies on the solid culture medium, which have developed after only 6 h of culturing for the most commonly encountered organisms. Due to the limited thickness of microcolonies, some of the underlying culture medium is sampled together with the bacteria. Spectra measured at different depths in a microcolony contain different amounts of the medium signal. A mathematical routine, involving vector algebra, is described for the nonsubjective correction of spectra for variable signal contributions of the medium. To illustrate the possibilities of our approach for the identification of microorganisms, Raman spectra were collected from 6-h microcolonies of five bacterial strains on solid culture medium. The classification results show that confocal Raman microspectroscopy has great potential as a powerful new tool in clinical diagnostic microbiology.

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confidence: 99%

Raman Spectroscopic Method for Identification of Clinically Relevant Microorganisms Growing on Solid Culture Medium

et al. 1999

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“…The fingerprints are obtained by detecting positive ions produced by electron impact fragmentation of the thermal fragmentation (pyrolysis) products of the matrices studied. This technique has proved to be a powerful tool for the characterization and identification of micro-organisms [5][6][7][8][9][10][11] and for solving problems of classification and recognition in various food sectors [12][13][14][15][16][17][18]. This technique has also been used successfully for quantitative measurements of the production of recombinant protein or indole in bacteria [19,20], for numeration of bacterial populations in pure cultures [21,22] or in dairy products [23] and for the prediction of sensory characteristics of dry cured hams [24].…”

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Evaluation of collagen and lipid contents and texture of meat by Curie point pyrolysis–mass spectrometry

Sebastian

Viallon-Fernandez

Tournayre

et al. 2004

Journal of Analytical and Applied Pyrolysis

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“…PyMS is a highly discriminatory method, which is applicable to any organic material. , The technique is well established within (micro)biology for the differentiation and identification of groups of bacteria, fungi, and yeasts. ,− and has also been applied to the authentication of foodstuffs. − …”

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Correction of Mass Spectral Drift Using Artificial Neural Networks

Goodacre

Kell

1996

Anal. Chem.

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For pyrolysis mass spectrometry (PyMS) to be used for the routine identification of microorganisms, for quantifying determinands in biological and biotechnological systems, and in the production of useful mass spectral libraries, it is paramount that newly acquired spectra be compared to those previously collected. Neural network and other multivariate calibration models have been used to relate mass spectra to the biological features of interest. As commonly observed, however, mass spectral fingerprints showed a lack of long-term reproducibility, due to instrumental drift in the mass spectrometer; when identical materials were analyzed by PyMS at dates from 4 to 20 months apart, neural network models produced at earlier times could not be used to give accurate estimates of determinand concentrations or bacterial identities. Neural networks, however, can be used to correct for pyrolysis mass spectrometer instrumental drift itself, so that neural network or other multivariate calibration models created using previously collected data can be used to give accurate estimates of determinand concentration or the nature of bacteria (or, indeed, other materials) from newly acquired pyrolysis mass spectra. This approach is not limited solely to pyrolysis mass spectrometry but is generally applicable to any analytical tool which is prone to instrumental drift, such as IR, ESR, NMR and other spectroscopies, and gas and liquid chromatography, as well as other types of mass spectrometry.

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Reading research in Great Britain — 1981

Cited by 3 publications

References 22 publications

Raman Spectroscopic Method for Identification of Clinically Relevant Microorganisms Growing on Solid Culture Medium

Raman Spectroscopic Method for Identification of Clinically Relevant Microorganisms Growing on Solid Culture Medium

Evaluation of collagen and lipid contents and texture of meat by Curie point pyrolysis–mass spectrometry

Correction of Mass Spectral Drift Using Artificial Neural Networks

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