Pattern Recognition Analysis of Metabolites in Escherichia coli Based on ESI‐Orbitrap Mass Spectrometry

To achieve rapid detection of carbapenem‐resistant Escherichia coli strains, a pattern recognition method based on electrospray ionization Orbitrap mass spectrometry (ESI‐Orbitrap MS) was used for the analysis of drug‐resistant, and sensitive strains of metabolites were analyzed. Results of five clu...

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Bibliographic Details
Published in:Chemistry & biodiversity 2023-05, Vol.20 (5), p.e202201153-n/a
Main Authors: Wang, Rui‐Yu, Yan, Yang‐Yang, Li, Bei, Fan, Xing, Gu, Bing, Zhou, Yuan, Sun, Bing‐Kang
Format: Article
Language:English
Subjects:
Chromatography, High Pressure Liquid - methods
Cluster Analysis
Clustering
Discriminant Analysis
E coli
Escherichia coli
Ionization
Least squares
Mass spectrometry
Mass spectroscopy
Metabolites
Metabolomics - methods
Pattern analysis
Pattern recognition
Principal Component Analysis
Principal components analysis
Spectrometry, Mass, Electrospray Ionization - methods
Statistical analysis
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Summary:To achieve rapid detection of carbapenem‐resistant Escherichia coli strains, a pattern recognition method based on electrospray ionization Orbitrap mass spectrometry (ESI‐Orbitrap MS) was used for the analysis of drug‐resistant, and sensitive strains of metabolites were analyzed. Results of five clustering methods applied to analytical data of metabolites were evaluated using iso‐phenotypic coefficients. The effectiveness of three methods, principal component analysis (PCA), partial least squares discriminant analysis (PLS‐DA) and orthogonal partial least squares discriminant analysis (OPLS‐DA), was compared. Univariate statistics such as t‐test and fold change were also used to examine the screened differential information. Both PLS‐DA and OPLS‐DA could achieve rapid identification of strain classes, and OPLS‐DA was more powerful in screening 96 significantly different ions. This work is expected to be useful for rapid and accurate identification of strains.
ISSN:1612-1872
1612-1880
DOI:10.1002/cbdv.202201153