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题名: Multi-metal element analysis for the identification of foodborne pathogenic bacteria
作者: Zhang, XX; Li, XL; Wang, YD; Xu, QL; Hu, J; Lu, JH
刊名: ANALYTICAL METHODS
出版日期: 2016
卷号: 8, 期号:27, 页码:5421-5426
DOI: 10.1039/c6ay01408a
通讯作者: Lu, JH (reprint author), Chinese Acad Sci, Shanghai Inst Appl Phys, CAS Key Lab Interfacial Phys & Technol, Div Phys Biol, Shanghai 201800, Peoples R China. ; Li, XL (reprint author), Shanghai Ind Technol Inst, Ctr Clin & Translat Med, Shanghai Acad Sci & Technol, Shanghai Ctr Bioinformat Technol, Shanghai 201203, Peoples R China.
文章类型: 期刊论文
英文摘要: Mineral element contents, combined with multivariate analysis, were used for the identification and classification of foodborne pathogens from a common genus (Rhodococcus equi, Staphylococcus spp., Listeria spp., Salmonella spp., Shigella spp., Escherichia coli, Enterobacter sakazakii, Yersinia enterocolitica and Vibrio spp.). 45 macro- and trace mineral elements of 30 foodborne pathogens were determined by a semiquantitative inductively coupled plasma mass spectrometry (SQ-ICP-MS) technique. The elemental analysis identified 10 significant elements (Si-28, Ca-43, Fe-57, Ti-47, Cr-52, Mn-55, Zn-66, Sr-88, Ba-137 and Pb-208) by ANOVA in different types of pathogens. Principal component analysis (PCA) reduced the 10 variables to 6 principal components which could explain 98.40% of the total variance. The classification models constructed by the Fisher linear discriminant analysis (Fisher LDA) and back-propagation artificial neural network (BP-ANN) achieved correctly classified rates of 86.9% and 91.3%, respectively. The results indicated that the combination of multi-metal element composition determination and multivariate analysis can be used as fingerprint to quickly identify and classify foodborne pathogens.
收录类别: SCI
语种: 英语
WOS记录号: WOS:000379480200010
ISSN号: 1759-9660
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.sinap.ac.cn/handle/331007/26580
Appears in Collections:中科院上海应用物理研究所2011-2017年_期刊论文

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