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


Amiali, N M; Mulvey, M R; Berger-Bächi, B; Sedman, J; Simor, A E; Ismail, A A (2008). Evaluation of Fourier transform infrared spectroscopy for the rapid identification of glycopeptide-intermediate Staphylococcus aureus. Journal of Antimicrobial Chemotherapy, 61(1):95-102.

Abstract

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.

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.

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6 citations in Web of Science®
8 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Medical Microbiology
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:2008
Deposited On:29 Aug 2008 14:49
Last Modified:05 Apr 2016 12:26
Publisher:Oxford University Press
ISSN:0305-7453
Publisher DOI:10.1093/jac/dkm400
PubMed ID:17962217

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