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Principal component analysis of normalized full spectrum mass spectrometry data in multiMS‐toolbox: An effective tool to identify important factors for classification of different metabolic patterns and bacterial strains
Author(s) -
Cejnar Pavel,
Kuckova Stepanka,
Prochazka Ales,
Karamonova Ludmila,
Svobodova Barbora
Publication year - 2018
Publication title -
rapid communications in mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 136
eISSN - 1097-0231
pISSN - 0951-4198
DOI - 10.1002/rcm.8110
Subject(s) - principal component analysis , enterobacter , normalization (sociology) , mass spectrometry , chemistry , agar , computational biology , chromatography , computer science , artificial intelligence , biology , escherichia coli , bacteria , biochemistry , genetics , sociology , anthropology , gene
Explorative statistical analysis of mass spectrometry data is still a time-consuming step. We analyzed critical factors for application of principal component analysis (PCA) in mass spectrometry and focused on two whole spectrum based normalization techniques and their application in the analysis of registered peak data and, in comparison, in full spectrum data analysis. We used this technique to identify different metabolic patterns in the bacterial culture of Cronobacter sakazakii, an important foodborne pathogen.