Liquid chromatography-mass spectrometry (LC-MS) untargeted experiments require complex chemometrics strategies to extract information from the experimental data. Here we discuss “data preprocessing”, the set of procedures performed on the raw data to produce a data matrix which will be the starting point for the subsequent statistical analysis. Data preprocessing is a crucial step on the path to knowledge extraction, which should be carefully controlled and optimized in order to maximize the output of any untargeted metabolomics investigation
Riccadonna, S.; Franceschi, P. (2018). Data treatment for LC-MS untargeted analysis. In: Metabolic profiling: methods and protocols (editor(s) Theodoridis, G.A.; Gika, H.G.; Wilson, I.D.). Berlin [etc.]: Springer. (METHODS IN MOLECULAR BIOLOGY): 27-39. ISBN: 9781493976423 doi: 10.1007/978-1-4939-7643-0_3. handle: http://hdl.handle.net/10449/36865
Data treatment for LC-MS untargeted analysis
Riccadonna, S.Primo
;Franceschi, P.
Ultimo
2018-01-01
Abstract
Liquid chromatography-mass spectrometry (LC-MS) untargeted experiments require complex chemometrics strategies to extract information from the experimental data. Here we discuss “data preprocessing”, the set of procedures performed on the raw data to produce a data matrix which will be the starting point for the subsequent statistical analysis. Data preprocessing is a crucial step on the path to knowledge extraction, which should be carefully controlled and optimized in order to maximize the output of any untargeted metabolomics investigation
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