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Surface-enhanced Raman spectroscopic technique for identification and classification of foodborne pathogens
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: 2022 ASABE Annual International Meeting 2200186.(doi:10.13031/aim.202200186)
Authors: Yahui Chen, Yankun Peng, Qinghui Guo
Keywords: SERS; Foodborne pathogenic bacteria; PCA; PLS-DA; Identification and classification
Abstract. Foodborne pathogenic bacteria are the source of foodborne diseases. Effective and rapid identification of foodborne pathogenic microorganisms is of great significance to ensure people's food safety. In this study, Surface-enhanced Raman Spectroscopic technique was used to detect the spectrograms of three foodborne pathogens (E. coli, L. monocytogenes, S. typhi). In order to obtain spectral data with higher intensity and better stability, Au@Ag NPs was selected as the enhanced substrate in the experiment. This method can not only obtain the enhancement effect of Ag NPs on bacterial detection, but also reduce the oxidation rate of Ag NPs due to the wrapping of Au NPs. Then, background removal, SG smoothing and baseline correction were used to preprocess and analyze the obtained spectral data to reduce the influence of noise signals on the final results. And principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to establish different classification models for the three pathogens and their species. The results of cross validation showed that PCA method with three principal components was effective in the identification of three pathogenic bacteria. The PLS-DA discriminant method with two latent variables has better performance in the discrimination of Gram-negative bacteria and Gram-positive bacteria, with sensitivity of 95.2% and accuracy of 99.9%. It can be seen that the SERS technology using Au@Ag NPs as the enhanced substrate can effectively enhance the spectral signal of pathogenic bacteria, and can also quickly identify and classify foodborne pathogenic bacteria by combining with different chemometrics methods.
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