Identification of metabolites from complex mixtures by 3D correlation of 1H NMR, MS and LC data using the SCORE-metabolite-ID approach.

Not only in metabolomics studies, but also in natural product chemistry, reliable identification of metabolites usually requires laborious steps of isolation and purification and remains a bottleneck in many studies. Direct metabolite identification from a complex mixture without individual isolation is therefore a preferred approach, but due to the large number of metabolites present in natural products, this approach is often hampered by signal overlap in the respective 1H NMR spectra. This paper presents a method for the three-dimensional mathematical correlation of NMR with MS data over the third dimension of the time course of a chromatographic fractionation. The MATLAB application SCORE-metabolite-ID (Semi-automatic COrrelation analysis for REliable metabolite IDentification) provides semi-automatic detection of correlated NMR and MS data, allowing NMR signals to be related to associated mass-to-charge ratios from ESI mass spectra. This approach 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 further tested on a polar extract of a pine nut sample. Straightforward identification of 40 metabolites could be shown, including the identification of ß-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.

Comparison of Regular, Pure Shift, and Fast 2D NMR Experiments for Determination of the Geographical Origin of Walnuts.

1H NMR spectroscopy, in combination with chemometric methods, was used to analyze the methanol/acetonitrile (1:1) extract of walnut (Juglans Regia L.) regarding the geographical origin of 128 authentic samples from different countries (France, Germany, China) and harvest years (2016-2019). Due to the large number of different metabolites within the acetonitrile/methanol extract, the one-dimensional (1D) 1H NOESY (nuclear Overhauser effect spectroscopy) spectra suffer from strongly overlapping signals. The identification of specific metabolites and statistical analysis are complicated. The use of pure shift 1H NMR spectra such as PSYCHE (pure shift yielded by chirp excitation) or two-dimensional ASAP-HSQC (acceleration by sharing adjacent polarization-heteronuclear single quantum correlation) spectra for multivariate analysis to determine the geographical origin of foods may be a promising method. Different types of NMR spectra (1D 1H NOESY, PSYCHE, and ASAP-HSQC) were acquired for each of the 128 walnut samples and the results of the statistical analysis were compared. A support vector machine classifier was applied for differentiation of samples from Germany/China, France/Germany, and France/China. The models obtained by conduction of a repeated nested cross-validation showed accuracies from 58.9% (±1.3%) to 95.9% (±0.8%). The potential of the 1H-13C HSQC as a 2D NMR experiment for metabolomics studies was shown.