Untargeted assignment and automatic integration of H 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 H 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 H NMR metabolomics datasets. In a first study, H 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 H NMR. In this work, MCR-ALS is shown to be a convenient tool for metabolite investigation and sample screening using H NMR, and it opens a new path for performing metabolomics studies with this chemometric technique.