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author:

Lin, Yongbin (Lin, Yongbin.) [1] | Wu, Zhiwei (Wu, Zhiwei.) [2]

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EI

Abstract:

Gram-negative bacteria had been regarded as several important sources of lethal infection. Rapid identification of pathogenic bacteria is extremely important for the diagnosis and clinical treatment of diseases. In current study, three gram-negative bacteria, including Klebsiella aerogenes, Enterobacter cloacae and Escherichia coli, were used to access the feasibility of characterizing Gram-negative bacteria by surface-enhanced Raman Spectroscopy (SERS). Bacterial samples were from Escherichia coli isolates (n=1000), Klebsiella aerogenes isolates (n=1000) and Enterobacter cloacaeand isolates (n=1000). The differences of three Gram-negative bacteria were characterized by SERS spectra. Furthermore, four multivariate statistical algorithms based on the combination of principal component analysis (or partial least squares) and linear discriminant analysis (or support vector machine) were used to discriminate the spectra of three gram-negative bacteria. © 2023 IOS Press. All rights reserved.

Keyword:

Diagnosis Discriminant analysis Escherichia coli Least squares approximations Light transmission Multivariant analysis Principal component analysis Raman spectroscopy Support vector machines

Community:

  • [ 1 ] [Lin, Yongbin]School of Advanced Manufacturing, Fuzhou University, Quanzhou; 362251, China
  • [ 2 ] [Wu, Zhiwei]Institute of Physics and Information Engineering, Quanzhou Normal University, Quanzhou; 362000, China

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Source :

ISSN: 0926-9630

Year: 2023

Volume: 308

Page: 253-260

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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