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A metabolomics approach combining [sup.1]H NMR and gas chromatography--electrospray ionization time-of-flight mass spectrometry (GC--EI-TOFMS) profiling was employed to characterize melon (Cucumis melo L.) fruit. In a first step, quantitative [sup.1]H NMR of polar extracts and principal component analyses (PCA) of the corresponding data highlighted the major metabolites in fruit flesh, including sugars, organic acids, and amino acids. In a second step, the spatial localization of metabolites was investigated using both analytical techniques. Direct [sup.1]H NMR profiling of juice or GC--EI-TOFMS profiling of tissue extracts collected from different locations in the fruit flesh provided information on advantages and drawbacks of each technique for the analysis of a sugar-rich matrix such as fruit. [sup.1]H NMR and GC--EI-TOFMS data sets were compared using independently performed PCA and multiblock hierarchical PCA (HPCA), respectively. In addition a correlation-based multiblock HPCA was used for direct comparison of both analytical data sets. These data analyses revealed several gradients of metabolites in fruit flesh which can be related with differences in metabolism and indicated the suitability of multiblock HPCA for correlation of data from two (or potentially more) metabolomics platforms.

Metabolomics plays a pivotal role in systems biology, and NMR is a central tool with high precision and exceptional resolution of chemical information. Most NMR metabolomic studies are based on 1 H 1D spectroscopy, severely limited by peak overlap. 13 C NMR benefits from a larger signal dispersion but is barely used in metabolomics due to ca. 6000-fold lower sensitivity. We introduce a new approach, based on hyperpolarized 13 C NMR at natural abundance, that circumvents this limitation. A new untargeted NMR-based metabolomic workflow based on dissolution dynamic nuclear polarization (d-DNP) for the first time enabled hyperpolarized natural abundance 13 C metabolomics. Statistical analysis of resulting hyperpolarized 13 C data distinguishes two groups of plant (tomato) extracts and highlights biomarkers, in full agreement with previous results on the same biological model. We also optimize parameters of the semiautomated d-DNP system suitable for high-throughput studies.

A metabolomics approach combining (1)H NMR and gas chromatography-electrospray ionization time-of-flight mass spectrometry (GC-EI-TOFMS) profiling was employed to characterize melon (Cucumis melo L.) fruit. In a first step, quantitative (1)H NMR of polar extracts and principal component analyses (PCA) of the corresponding data highlighted the major metabolites in fruit flesh, including sugars, organic acids, and amino acids. In a second step, the spatial localization of metabolites was investigated using both analytical techniques. Direct (1)H NMR profiling of juice or GC-EI-TOFMS profiling of tissue extracts collected from different locations in the fruit flesh provided information on advantages and drawbacks of each technique for the analysis of a sugar-rich matrix such as fruit. (1)H NMR and GC-EI-TOFMS data sets were compared using independently performed PCA and multiblock hierarchical PCA (HPCA), respectively. In addition a correlation-based multiblock HPCA was used for direct comparison of both analytical data sets. These data analyses revealed several gradients of metabolites in fruit flesh which can be related with differences in metabolism and indicated the suitability of multiblock HPCA for correlation of data from two (or potentially more) metabolomics platforms.