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RAPID IDENTIFICATION AND CLASSIFICATION OF STAPHYLOCOCCUS AUREUS BY ATTENUATED TOTAL REFLECTANCE FOURIER TRANSFORM INFRARED SPECTROSCOPY
Author(s) -
XIE YANPING,
XU SHUJUN,
HU YU,
CHEN WANYI,
HE YIPING,
SHI XIANMING
Publication year - 2012
Publication title -
journal of food safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.427
H-Index - 43
eISSN - 1745-4565
pISSN - 0149-6085
DOI - 10.1111/j.1745-4565.2012.00365.x
Subject(s) - staphylococcus aureus , staphylococcus haemolyticus , partial least squares regression , fourier transform infrared spectroscopy , fourier transform , microbiology and biotechnology , biology , staphylococcus , mathematics , bacteria , physics , optics , statistics , genetics , mathematical analysis
Staphylococcus aureus is an important bacterium that can cause serious infections in human such as pneumonia and bacteremia. Rapid detection of this pathogen is crucial in food industries and clinical laboratories to control S. aureus food poisoning and human infections. In this study, Fourier transform infrared spectroscopy equipped with a germanium attenuated total reflection accessory was used as a novel approach to identify S. aureus . A total of 17 reference strains belonging to 4 different species and 84 clinical isolates of Staphylococcus spp. were analyzed. After the cultivation of the strains, spectral collection and data preprocessing, the S. aureus isolates were identified by a two‐step discrimination procedure. An internal validation and the related external validation were performed to demonstrate the discriminatory power and the quality of the discrimination models before the discrimination analysis. In the first step, 38 S. aureus isolates were correctly classified and the others were misidentified as Staphylococcus haemolyticus by hierarchical clustering analysis model using the first derivatives from the spectral range between 1,800 and 1,050/cm. In the second step, several classification/discrimination algorithms of soft‐independent modeling of class analogy, principal component regression and partial least squares regression (PLSR) were applied to build models for differentiating S. aureus and S. haemolyticus . The results showed that 57 (98.3%) strains and 4 (100%) strains of S. aureus and S. haemolyticus could be correctly identified by PLSR. PRACTICAL APPLICATIONS Fourier transform infrared (FTIR) spectroscopy is a potential method for rapid discrimination, classification and identification of intact microbial cells. In this study, FTIR spectroscopy equipped with a germanium attenuated total reflection accessory, using hierarchical clustering analysis–partial least squares regression discrimination analysis, is a powerful means for routine identification of Staphylococcus aureus .