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The 'hydrological connectivity inside the soil' refers to both the spatial pattern inside the soil (structural component) and the physical–chemical process at a molecular level (functional component). Fast field cycling (FFC) nuclear magnetic resonance (NMR) relaxometry allows for measuring structural and functional connectivity by two suitable indexes named structural connectivity index (SCI) and functional connectivity index (FCI). In this study, FFC NMR relaxometry was applied to soils sampled in a very degraded environment (i.e., a badland area) to detect the capability of the measurement technique to distinguish the hydrological connectivity of these samples having different conditions (layer explored by roots, sparsely‐vegetated and bare soil). The relaxograms measured by the FFC NMR, using Proton Larmor frequencies in the range 0.01–10 MHz, were integrated and the resulting S‐shaped curves were analysed to obtain the connectivity indexes. Results showed that the 'Sparsely vegetated' sample is characterized by more small‐sized pores than the 'Rooted' one. The comparison between the 'Sparsely vegetated' and 'Bare' conditions pointed out that the presence of vegetation reduces the measured relaxation times and, as a consequence, the corresponding pore sizes and modifies the structural connectivity. The analysis also revealed that the three samples are characterized by similar values of SCI, which are independent of the proton Larmor frequency, while the FCI values of the 'Bare' soil are the lowest. Conversely, samples from soil with vegetation ('Rooted' and 'Sparsely vegetated') present comparable functional connectivity. Finally, the analysis of the frequency distribution of the ratio of each connectivity index and its mean value (SCI/m(SCI) and FCI/m(FCI)) allowed to establish its normal distribution. For the investigated samples, this result established that FCI and SCI can be represented by their mean value. [ABSTRACT FROM AUTHOR]

In this study, 1H-NMR spectroscopy coupled with chemometrics was applied to study the wine metabolome and to classify wines according to different grape varieties and different terroirs. By obtaining the metabolomic fingerprinting and profiling of the wines, it was possible to assess the metabolic biomarkers leading the classification (i.e., phenolic compounds, aroma compounds, amino acids, and organic acids). Moreover, information about the influence of the soil in shaping wine metabolome was obtained. For instance, the relationship between the soil texture and the content of amino acids and organic acids in wines was highlighted. The analysis conducted in this study allowed extraction of relevant spectral information not only from the most populated and concentrated spectral areas (e.g., aliphatic and carbinolic areas), but also from crowded spectral areas held by lowly concentrated compounds (i.e., polyphenols). This may be due to a successful combination between the parameters used for data reduction, preprocessing and elaboration. The metabolomic fingerprinting also allowed exploration of the H-bonds network inside the wines, which affects both gustatory and olfactory perceptions, by modulating the way how solutes interact with the human sensory receptors. These findings may have important implications in the context of food traceability and quality control, providing information about the chemical composition and biomolecular markers from a holistic point of view. [ABSTRACT FROM AUTHOR]

Summary: In this study, the soil effect on the chemical composition of red grapes from Vitis vinifera L. (cv Nero d'Avola) was studied. The soil effect was evaluated by considering the main soil chemical–physical parameters (e.g., texture, pH, total carbonates, cation exchange capacity, electric conductivity, organic matter, and mineral composition) of four vineyards located in the southwestern coast of Sicily (southern Italy). Grapes' quality in relation to different soils was investigated by studying some composition parameters and the (in)/homogeneity of ripeness of grapes at the harvest date. Results suggested that the soil greatly affected the quality of Nero d'Avola red grapes. The most impactful soil parameters were cation exchange capacity, electric conductivity, pH, and mineral composition (especially P and Mn content) that are directly involved into grapevine nutrition. Through multivariate statistical analysis, it was highlighted that the soil effect was remarkable within a specific vintage. However, when more vintages are considered simultaneously, the vintage effect (conceivably through the inter‐annual climatic variability) is the major factor determining grapes quality. [ABSTRACT FROM AUTHOR]

Focusing on the idea that multivalent cations affect SOM matrix and surface, we treated peat and soil samples by solutions of NaCl, CaCl2 or AlCl3. Water binding was characterized with low field 1H-NMR-relaxometry (20 MHz) and 1H wideline NMR spectroscopy (400 MHz) and compared to contact angles.

In this study, the metabolome of different types of tea (i.e., black, green and earl grey) is explored by means of HRMAS 1H (i.e., semisolid state) NMR and CPMAS 13C (i.e., solid state) NMR spectroscopies. By elaborating the metabolomic data with unsupervised and supervised chemometric tools (PCA, PLS-DA), it was possible to set up classification models with the aim to discriminate the different types of tea as based on differences in their chemical composition. Both the applications of the NMR spectroscopies also allowed to obtain information about the metabolic biomarkers leading the differentiation among teas. These were mainly represented by phenolic compounds. Also, some non-phenolic compounds, such as amino acids, carbohydrates, and terpenoids, played important roles in shaping tea quality. The findings of this study provided useful insights into the application of solid and semisolid state NMR spectroscopies, in combination with chemometrics, in the context of food authentication and traceability. [Display omitted] • The metabolome of black, green, and earl grey teas was explored. • Tea metabolome was investigated by means of solid and semisolid state NMR spectroscopy. • Chemometric models were able to classify teas according to specific metabolic biomarkers. • Classification models can be used for traceability and authenticity purposes. [ABSTRACT FROM AUTHOR]