DETECTION AND DISCRIMINATION OF ENTEROBACTER SAKAZAKII (CRONOBACTER SPP.) BY MID-INFRARED SPECTROSCOPY AND MULTIVARIATE STATISTICAL ANALYSES

Authors


TEL: 573-884-6718; FAX: 573-884-7964; EMAIL: linme@missouri.edu

Abstract

ABSTRACT

Enterobacter sakazakii can cause rare but life-threatening diseases such as meningitis in infants and neonates. Fourier transform infrared (FT-IR) spectroscopy was used to detect and discriminate between eight E. sakazakii strains, two Enterobacter cloacae strains, three Escherichia coli strains and two Klebsiella pneumoniae strains. FT-IR vibrational combination bands reflect subtle compositional differences in the cell membranes of E. sakazakii strains, especially in the region between 1,200 and 900 cm1which contains absorption bands from carbohydrates. Two multivariate statistical analyses including principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) were used for data analysis. E. sakazakii strains were clearly distinguishable from the other strains by PCA. Based upon SIMCA analysis, 90% of E. sakazakii, 88% of E. cloacae, 91% of E. coli and 91% of K. pneumoniae samples were correctly classified, suggesting that this technique could be used to detect E. sakazakii strains rapidly and accurately.

PRACTICAL APPLICATIONS

Fourier transform infrared (FT-IR) coupled with multivariate statistical analyses can be used to detect, discriminate and identify Enterobacter sakazakii strains that have been implicated in food safety incidents caused by contaminated infant formula. Compared with traditional microbiological plating methods, this new approach of using FT-IR could be an alternative means for rapid and accurate detection of bacterial samples that are important in agricultural, food and medical areas.

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