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Evaluation of Fourier transform infrared spectroscopy for the rapid identification of glycopeptide-intermediate Staphylococcus aureus

Amiali, Nassim M. ; Mulvey, Michael R. ; Berger-Bächi, Brigitte ; Sedman, Jacqueline ; Simor, Andrew E. ; Ismail, Ashraf A.

In: Journal of Antimicrobial Chemotherapy, 2008, vol. 61, no. 1, p. 95-102

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    Titel
    • Evaluation of Fourier transform infrared spectroscopy for the rapid identification of glycopeptide-intermediate Staphylococcus aureus
    Autor
    • Amiali, Nassim M.. Quelab Laboratories Inc., Montreal, QC, Canada
    • Mulvey, Michael R.. Nosocomial Infections, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada
    • Berger-Bächi, Brigitte. Institute of Medical Microbiology, University of Zürich, Switzerland
    • Sedman, Jacqueline. McGill IR Group, McGill University, Montreal, QC, Canada
    • Simor, Andrew E.. Sunnybrook and Women's College, Health Science Center, Toronto, ON, Canada
    • Ismail, Ashraf A.. McGill IR Group, McGill University, Montreal, QC, Canada
    Dokumententyp
    • Postprint
    Sprache
    • Englisch
    Veröffentlicht in
    • Journal of Antimicrobial Chemotherapy, 2008, vol. 61, no. 1, p. 95-102. Oxford University Press
    Andere elektronische Ausgabe
    OAI-PMH-ID
    • oai:doc.rero.ch:304903
    Summary
    Objectives To evaluate Fourier transform infrared (FTIR) spectroscopy as a rapid method for distinguishing glycopeptide-intermediate Staphylococcus aureus (GISA) from glycopeptide-susceptible methicillin-resistant S. aureus (MRSA) and to compare three data analysis methods. Methods First-derivative normalized spectra of dried films of bacterial growth on Que-Bact® Universal Medium No. 2 were examined by singular value decomposition to identify key spectral regions. Region selection was analysed by principal component analysis (PCA), self-organizing maps (SOMs) and the K-nearest neighbour (KNN) algorithm. The initial data set included 35 GISA (including GISA Mu50 and heterogeneous GISA Mu3) and 25 epidemic MRSA. The regions were then tested using enlarged data sets that included 22 sporadic and 85 additional epidemic MRSA. Results Epidemic MRSA and GISA/hGISA were separated into two distinct clusters on the basis of spectral data from regions 1352-1315 and 1480-1460 cm−1, the former providing 100% correct classification by all three analyses and the latter providing 96.67% correct by PCA, 98.34% by SOM and 100% by KNN. The 1480-1460 cm−1 region was more effective for distinguishing GISA/hGISA from a set combining sporadic and epidemic MRSA, with two GISA/hGISA and four sporadic MRSA misclassified by PCA and SOM (92.69% correct), while the KNN method misclassified three of the four sporadic MRSA (93.90% correct). The addition of 85 other epidemic MRSA this set increased the fraction of correctly classified isolates to 96.41% and 97.01% by PCA, SOM and KNN, respectively. Conclusions As only 6 of 167 isolates were misclassified, FTIR spectroscopy may provide means of rapid and accurate identification of GISA and hGISA among isolates of MRSA