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 pre...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/270270 |
| Acceso en línea: | http://hdl.handle.net/10261/270270 https://api.elsevier.com/content/abstract/scopus_id/85013392592 |
| Access Level: | acceso abierto |
| Palabra clave: | Nuclear magnetic resonance Metabolomics Multivariate curve resolution |
| Sumario: | 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. |
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