¹H NMR-Based Metabolomics to Assess the Impact of Soil Type on the Chemical Composition of Nero d’Avola Red Wines

Abstract

In this study, the soil effect on the micro-component composition of Nero d’Avola wines obtained from different locations was investigated through 1H NMR-based metabolomics. Two different approaches were applied: the targeted (TA) and the non-targeted one (NTA). The former differentiated the wines by profiling (i.e., by identifying and quantifying) a number of different metabolites. The latter provided wine fingerprinting by processing the entire spectra with multivariate statistical analysis. NTA also allowed investigation of the hydrogen bond network inside wines via the analysis of 1H NMR chemical shift dispersions. Results showed that the differences among wines were due not only to the concentrations of various analytes but also to the characteristics of the H-bond network where different solutes were involved. The H-bond network affects both gustatory and olfactory perceptions by modulating the way how solutes interact with the human sensorial receptors. Moreover, the aforementioned H-bond network is also related to the soil properties from which the grapes were taken. Therefore, the present study can be considered a good attempt to investigate terroir, i.e., the relationship between wine quality and soil characteristics.

1H-NMR Spectroscopy Coupled with Chemometrics to Classify Wines According to Different Grape Varieties and Different Terroirs

“…To attain this aim, the metabolomic analysis of wines from two Vitis vinifera L. grape varieties (e.g., Nero d'Avola red and Grillo white cultivars) was carried out. Moreover, in order to further verify the findings of our previous studies [26][27][28], the second goal was to investigate the effect of the soil on the metabolome of white wines produced from Vitis vinifera L. cv Grillo grapes. The effects of the main soil chemical-physical parameters (namely texture, pH, total carbonates, cation exchange capacity, electric conductivity, organic matter, and mineral composition) on the determination of wine metabolome were investigated.…”

“…Grapes from each vineyard were manually harvested in September 2021, when about 200 g L −1 of fermentable sugars were accumulated in berries. The red wines, made from Nero d'Avola grapes, were obtained by applying the vinification process described in Bambina et al [26]. Briefly, 150 kg of grapes for each trial were destemmed and softly crushed.…”

“…The targeted analysis provided the wine profiling by identifying and quantifying an ensemble of metabolites. Both type of analyses have been already described in Bambina et al [26]. The input variables for the untargeted analysis were generated via bucketing the spectra.…”

“…However, the mechanisms regulating the effects of the soil on wine quality have been only moderately addressed [24,25]. In one of our previous studies [26], we employed 1 H-NMR-based metabolomics to classify Nero d'Avola red wines from different terroirs. In particular, we focused on the influence of the soil in shaping the micro-component composition of Nero d'Avola wines.…”

Direct Identification and Quantitation of Protein Peptide Powders Based on Multi-Molecular Infrared Spectroscopy and Multivariate Data Fusion

“…It integrated one-dimensional IR spectroscopy, second derivative infrared (SD-IR) spectroscopy, and two-dimensional correlation IR spectroscopy (2DCOS-IR) with progressively higher resolution, and it employed to directly identify flour with diverse harmful substances, which is more suitable for the establishment of the model and has high accuracy . Chemometrics maximized the extraction of useful chemical information to achieve a better description and prediction of the natural sciences, which was widely used as a multivariate statistical analysis tool for food quality evaluation and identification, including adulteration identification, traceability, and grade evaluation − generally by building classification and regression models for qualitative and quantitative analyses, such as principal component analysis (PCA), support vector machine (SVM), k -nearest neighbor (KNN), back propagation neural network (BPNN), and partial least squares regression (PLSR). ,− …”