Due to the large number of metabolites of various compound classes present in natural products, which in addition occur in high concentration differences, the identification of individual metabolites from either 1H NMR spectra or MS spectra is hardly possible due to signal overlap and the lack of information from interrelated signals of the same compound. This paper presents a method for the three-dimensional correlation of NMR and MS data over the third dimension of the time course of a chromatographic fractionation. Compounds do not need to be isolated individually, but the NMR and MS signals of the individual compounds can be correlated mathematically. The app SCORE-metabolite-ID (Semi-automatic COrrelation analysis for REliable metabolite IDentification) was implemented in MATLAB and provides semi-automatic detection of correlated NMR and MS data. Thereby, the app enables fast and reliable dereplication of known metabolites and facilitates the dynamic analysis for the identification of unknown compounds in any complex mixture. The strategy was validated using an artificial mixture and tested further on a polar extract of a pine nut sample. Straightforward identification of 40 metabolites could be shown, including the identification of β-D-glucopyranosyl-1-N-indole-3-acetyl-N-L-aspartic acid (1) and Nα-(2-hydroxy-2-carboxymethylsuccinyl)-L-arginine (2), the latter being identified in a food sample for the first time.
Name of App changed; 13C data of compound 2 has been added; Introduction and literature has been updated to molecular networking methods
SCORE-metabolite-ID: Semi-automatic and reliable identification of metabolites from complex mixtures by correlation of 1H NMR, MS and LC data