Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach

•An untargeted resolution approach of 1H NMR metabolomics datasets is proposed.•The approach uses MCR-ALS combined with the application of equality constraints.•Equality constraints were designed based on observed proton inter-correlations.•This strategy was validated with simulated and real 1H NMR metabolomics datasets.

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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