Abstract

<i>Salmonella enterica</i> serovars include pathogens responsible for high numbers of foodborne salmonellosis. Fourier transform infrared (FT-IR) spectroscopy can be used to rapidly and accurately identify microorganisms based on unique spectra of bacterial cell components. The objectives of this study were to discriminate closely related <i>Salmonella enterica</i> serovars by using FT-IR spectroscopy and multivariate analysis and to compare the performance of three techniques for differentiating among <i>Salmonella</i> serovars. Selected serovars of <i>S. enterica</i> were streaked onto plate count agar and incubated (37 °C, 24 h). Isolated colonies were suspended in phosphate buffer or 50% ethanol (10 μL). Suspensions were placed on (1) ZnSe crystals for transmission, (2) disposable polyethylene membranes (DPM) for transmission, and (3) diamond crystal plate for attenuated total reflectance (ATR) analyses; all samples were dried under vacuum. Classification models, soft independent modeling of class analogy (SIMCA), from derivatized infrared spectra (1300–900 cm<sup>−1</sup>), discriminated among <i>Salmonella</i> serovars presumably attributed to cell's lipopolysaccharides (1000–980 cm<sup>−1</sup>). Samples on DPM required high cell density for reliable spectra. High-quality spectra were obtained when a single colony was suspended in ethanol or buffer and mounted on ZnSe crystals for transmission or diamond plate for ATR analysis. Prediction of unknowns, representative of serovars used to construct classification models, showed that all techniques were suitable for the rapid and accurate differentiation of <i>Salmonella</i> serovars.

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