Submitted to: Journal of Food Safety
Publication Type: Peer reviewed journal
Publication Acceptance Date: 1/9/2012
Publication Date: 4/17/2012
Citation: Xie, Y., Xu, S., Hu, Y., Chen, W., He, Y., Shi, X. 2012. Rapid identification and classification of Staphylococcus aureus by attenuated total reflectance fourier transform infrared spectroscopy. Journal of Food Safety. 32(2):176-183. Interpretive Summary: Fourier transform infrared (FTIR) spectroscopy is a potential method for rapid discrimination, classification and identification of intact bacterial cells. The identification of unknown bacterial strains is based on an extensive reference library constructed from reference strains. In this study, a rapid and inexpensive method for specifically discriminating Staphylococcus aureus from other staphylococcal species and non-staphylococci bacteria was developed by using FTIR spectroscopy equipped with a germanium attenuated total reflection (ATR) accessory. The ATR-FTIR spectra combined with a discrimination algorithm for data analysis was determined to be a powerful means for routine identification of S. aureus isolates. It could potentially be used in the food industry and in clinical laboratories to monitor for microbial contamination, either alone or combination with other techniques.
Technical Abstract: Staphylococcus aureus is an important bacterium that can cause serious infections in humans 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 (FTIR) spectroscopy equipped with a germanium attenuated total reflection (ATR) accessory was used as a novel approach to identify S. aureus. A total of 84 clinical isolates of Staphylococcus spp. and 17 reference strains belonging to 4 different species 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 S. haemolyticus by hierarchical clustering analysis (HCA) model using the first derivatives from the spectral range between 1800 and 1050 cm-1. In the second step, several classification/discrimination algorithms of soft independent modeling of class analogy (SIMCA), principal component regression (PCR+) 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, respectively, could be correctly identified by PLSR.