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.

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Wenn Betrüger Haselnüsse mit Erdnüssen verfälschen und so einen höheren Preis erzielen, ist das ein lukratives Geschäft. Ob Lebensmittel authentisch sind, lässt sich anhand ihrer Metaboliten überprüfen, denn diese zeigen ein charakteristisches NMR‐Spektrum.

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.

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 SMART-metabolite-ID (Semi-automatic Mixture Analysis for Reliable and direcT 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.