Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach
In this article, we propose the use of the Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) chemometrics method to resolve the 1H NMR spectra and concentration of the individual metabolites in their mixtures in untargeted metabolomics studies. A decision tree-based strategy is presented to optimally select and implement spectra estimates and equality constraints during MCR-ALS optimization. The proposed method has been satisfactorily evaluated using different 1H NMR metabolomics datasets. In a first study, 1H NMR spectra of the metabolites in a simulated mixture were successfully recovered and assigned. In a second study, more than 30 metabolites were characterized and quantified from an experimental unknown mixture analyzed by 1H NMR. In this work, MCR-ALS is shown to be a convenient tool for metabolite investigation and sample screening using 1H NMR, and it opens a new path for performing metabolomics studies with this chemometric technique.
| Main Authors: | , , |
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| Other Authors: | |
| Format: | artículo biblioteca |
| Language: | English |
| Published: |
Elsevier
2017-04-29
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| Subjects: | Nuclear magnetic resonance, Metabolomics, Multivariate curve resolution, |
| Online Access: | http://hdl.handle.net/10261/270270 http://dx.doi.org/10.13039/501100000781 https://api.elsevier.com/content/abstract/scopus_id/85013392592 |
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