Metabolomic by 1H NMR spectroscopy differentiates "Fiano di Avellino" white wines obtained with different yeast strains

Preliminary Characterization of "Salice Salentino" PDO Wines from Salento (South Italy) Negroamaro Grapes: NMR-Based Metabolomic and Biotoxicological Analyses

(1) Background: A preliminary investigation of Protected Designation of Origin (PDO) wines (red and rosé) produced from Negroamaro grapes-a native Salento (Apulia, Southern Italy) vine that is part of the Salice s.no PDO area-was performed in this work. (2) Methods: 1H-NMR spectroscopy, in combination with multivariate statistical analysis (MVA), was employed to characterize the metabolic profiles of 39 wine samples. Spectrophotometric methods were used to obtain preliminary information on the phenolic composition of wines and the associated antioxidant activity. The HepG2 liver cell line was used to assess the biological activity (effect on cell viability and genotoxicity activity) of wine samples. (3) Results: The NMR spectra analysis revealed the presence of signals ascribable to phenolic compounds such as gallic, hydroxycinnamic, and syringic acids. Relative content of these metabolites has been shown to be higher in red than in rosés wines and related to the wine producers. Interestingly, a similar pattern was observed in biological analyses. Red wines compared to the rosé wines display great variations in antioxidant capacity when evaluated as fresh samples using the DPPH and ORAC methods. Furthermore, all red wines exhibited a concentration-dependent decrease in cellular viability and live cells; this phenomenon is much less pronounced in rosé wines. (4) Conclusions: The resulting findings from this study reveal that winemaking operations could lead to final products with different chemical compositions and related properties. Even when starting from the same crop variety and cultivation region, significant differences were observed in the wine samples NMR-metabolic profiles and in vitro biotoxicological activity.

Fingerprinting and profiling in metabolomics of biosamples

This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.

Current trends in ŒNO-NMR based metabolomics

Present work discusses strengths and limitations of two Nuclear Magnetic Resonance outliers obtained with a water-to-ethanol solvent multi pre saturation acquisition method, recently included in the Compendium of International Methods of Analysis of Wines and Musts, published as OIV-MA-AS316-01, and their accuracy for metabolomics analysis. Furthermore, it is also presented an alternative to produce more discriminant and sensitive NMR data matrices for metabolomics studies, comprising the use of a novel NMR acquisition strategy in wines, the double pulsed-field gradient echo (DPFGE) NMR scheme, with a refocusing band-selective uniform-response pure-phase selective pulse, for a selective excitation of the 5-10 ppm chemical shift range of wine samples, that reveals novel broad aromatic 1H resonances, directly associated to complex polyphenols. Both aromatics and full binned OIV-MA-AS316-01,as well as the selective 5-10 ppm DPFGE NMR outliers were statistically analyzed with diverse non-supervised Principal Component Analysis (PCA) and supervised Partial Least Squares -Discriminant Analysis (PLS-DA), sparse (sPLS-DA) least squares- discriminant analysis, and orthogonal projections to latent structures discriminant analysis (OPLS-DA). Supervised multivariate statistical analysis of DPFGE and aromatics’ binned OIV-MA-AS316-01NMR data have shown their robustness to broadly discriminate geographical origins and narrowly differentiate between different fermentation schemes of wines from identical variety and region.

1H-NMR Metabolomics as a Tool for Winemaking Monitoring

The chemical composition of wine is known to be influenced by multiple factors including some viticulture practices and winemaking processes. 1H-NMR metabolomics has been successfully applied to the study of wine authenticity. In the present study, 1H-NMR metabolomics in combination with multivariate analysis was applied to investigate the effects of grape maturity and enzyme and fining treatments on Cabernet Sauvignon wines. A total of forty wine metabolites were quantified. Three different stages of maturity were studied (under-maturity, maturity and over-maturity). Enzyme treatments were carried out using two pectolytic enzymes (E1 and E2). Finally, two proteinaceous fining treatments were compared (vegetable protein, fining F1; pea protein and PVPP, fining F2). The results show a clear difference between the three stages of maturity, with an impact on different classes of metabolites including amino acids, organic acids, sugars, phenolic compounds, alcohols and esters. A clear separation between enzymes E1 and E2 was observed. Both fining agents had a significant effect on metabolite concentrations. The results demonstrate that 1H-NMR metabolomics provides a fast and robust approach to study the effect of winemaking processes on wine metabolites. These results support the interest to pursue the development of 1H-NMR metabolomics to investigate the effects of winemaking on wine quality.

Nuclear Magnetic Resonance Metabolomics with Double Pulsed-Field-Gradient Echo and Automatized Solvent Suppression Spectroscopy for Multivariate Data Matrix Applied in Novel Wine and Juice Discriminant Analysis

The quality of foods has led researchers to use various analytical methods to determine the amounts of principal food constituents; some of them are the NMR techniques with a multivariate statistical analysis (NMR-MSA). The present work introduces a set of NMR-MSA novelties. First, the use of a double pulsed-field-gradient echo (DPFGE) experiment with a refocusing band-selective uniform response pure-phase selective pulse for the selective excitation of a 5–10-ppm range of wine samples reveals novel broad ¹H resonances. Second, an NMR-MSA foodomics approach to discriminate between wine samples produced from the same Cabernet Sauvignon variety fermented with different yeast strains proposed for large-scale alcohol reductions. Third a comparative study between a nonsupervised Principal Component Analysis (PCA), supervised standard partial (PLS-DA), and sparse (sPLS-DA) least squares discriminant analysis, as well as orthogonal projections to a latent structures discriminant analysis (OPLS-DA), for obtaining holistic fingerprints. The MSA discriminated between different Cabernet Sauvignon fermentation schemes and juice varieties (apple, apricot, and orange) or juice authentications (puree, nectar, concentrated, and commercial juice fruit drinks). The new pulse sequence DPFGE demonstrated an enhanced sensitivity in the aromatic zone of wine samples, allowing a better application of different unsupervised and supervised multivariate statistical analysis approaches.

Non-conventional yeasts from fermented honey by-products: Focus on Hanseniaspora uvarum strains for craft beer production

The increasing interest in novel beer productions focused on non-Saccharomyces yeasts in order to pursue their potential in generating groundbreaking sensory profiles. Traditional fermented beverages represent an important source of yeast strains which could express interesting features during brewing. A total of 404 yeasts were isolated from fermented honey by-products and identified as Saccharomyces cerevisiae, Wickerhamomyces anomalus, Zygosaccharomyces bailii, Zygosaccharomyces rouxii and Hanseniaspora uvarum. Five H. uvarum strains were screened for their brewing capability. Interestingly, Hanseniaspora uvarum strains showed growth in presence of ethanol and hop and a more rapid growth than the control strain S. cerevisiae US-05. Even though all strains showed a very low fermentation power, their concentrations ranged between 7 and 8 Log cycles during fermentation. The statistical analyses showed significant differences among the strains and underlined the ability of YGA2 and YGA34 to grow rapidly in presence of ethanol and hop. The strain YGA34 showed the best technological properties and was selected for beer production. Its presence in mixed- and sequential-culture fermentations with US-05 did not influence attenuation and ethanol concentration but had a significant impact on glycerol and acetic acid concentrations, with a higher sensory complexity and intensity, representing promising co-starters during craft beer production.

Comparative NMR Metabolomics Profiling between Mexican Ancestral & Artisanal Mezcals and Industrialized Wines to Discriminate Geographical Origins, Agave Species or Grape Varieties and Manufacturing Processes as a Function of Their Quality Attributes

The oenological industry has benefited from the use of Nuclear Magnetic Resonance (¹H-NMR) spectroscopy in combination with Multivariate Statistical Analysis (MSA) as a foodomics tool for retrieving discriminant features related to geographical origins, grape varieties, and further quality controls. Said omics methods have gained such attention that Intergovernmental Organizations and Control Agencies are currently recommending their massive use amongst countries as quality compliances for tracking standard and degradation parameters, fermentation products, polyphenols, amino acids, geographical origins, appellations d’origine contrôlée and type of monovarietal strains in wines. This study presents, for the first time, a ¹H-NMR/MSA profiling of industrial Mexican wines, finding excellent statistical features to discriminate between oenological regions and grape varieties with supervised Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA). In a comparative way, it is applied with the ¹H-NMR/OPLS-DA workflow for the first time in ancestral and artisanal Mexican mezcals with promising results to discriminate between regions, agave species and manufacturing processes. The central aim of this comparative study is to extrapolate the know-how of wine-omics into the non-professionalized mezcal industry for establishing the NMR acquisition, preprocessing and statistical analysis basis to implement novel, non-invasive and highly reproducible regional, agave species and manufacturing-quality controls.

NMR in the Service of Wine Differentiation

NMR is a swift and highly reproducible spectrometric technique that makes it possible to obtain spectra containing a lot of information about the sample analyzed. This approach helps major components be described in complex mixtures such as wine in just one analysis. Analysis of wine metabolites is very often used to understand the impact of geographical origin or variety on wine quality. NMR is often used for tracing the geographical origin of wine. Research on NMR metabolic effects of geographical origin is of great importance as the high added value of wines results from compliance with state legislation on the protected denomination of origin (PDO) and protected geographical indication (PGI) for the administration of the appellation of wines. A review of NMR with emphasis on SNIF-NMR in the analysis of wine authenticity is given. SNIF-NMR remains a method of choice for the detection of wine chaptalization as it is the only approach which provides position-specific information on the origin of sugar in wine. However, the sample preparation step, which lacks major improvements since its conception, is strenuous and expensive, and suffers from drawbacks in terms of low sample throughput. Mainstream 1D and 2D NMR experiments provide a fast and affordable way to authenticate wine based on the geographical origin, vintage, and variety discrimination, and include a simple and non-destructive sample preparation step. With this approach, spectral data processing often represents a crucial step of the analysis. With properly performed NMR experiments good to excellent differentiation of wines from different vintages, regions, and varieties was achieved recently.

An overview of chemical contaminants and other undesirable chemicals in alcoholic beverages and strategies for analysis

The presence of chemical contaminant in alcoholic beverages is a widespread and notable problem with potential implications for human health. With the complexity and wide variation in the raw materials, production processes, and contact materials involved, there are a multitude of opportunities for a diverse host of undesirable compounds to make their way into the final product-some of which may currently remain unidentified and undetected. This review provides an overview of the notable contaminants (including pesticides, environmental contaminants, mycotoxins, process-induced contaminants, residues of food contact material [FCM], and illegal additives) that have been detected in alcoholic products thus far based on prior reviews and findings in the literature, and will additionally consider the potential sources for contamination, and finally discuss and identify gaps in current analytical strategies. The findings of this review highlight a need for further investigation into unwanted substances in alcoholic beverages, particularly concerning chemical migrants from FCMs, as well as a need for comprehensive nontargeted analytical techniques capable of determining unanticipated contaminants.

Wine traceability and authenticity: approaches for geographical origin, variety and vintage assessment

The aim of this work is to identify and discuss physicochemical wine characteristics, to provide to some extent a link to the vintage, variety, and/or geographical origin. Bibliographic datasets were attempted to provide the main information for topic comprehension, identifying the sources of wine compositional variability and how these can be expressed in terms of the belonging categories. Since all the environmental and technological conditions which vineyard and wine are subjected are rarely known, different sources were inspected. Great importance was given to the study of isotopic composition because of its importance in food frauds detection history. The interaction of the plant genotype with the environmental conditions of the vintage is the main responsible for the wines organic and inorganic fraction variability in terms of both total and relative content. This phenotypical expression, together with human and abiotic variability sources, has been examined since it contains to some extent the information for the discrimination of wines according to their category. Recently, new proton nuclear magnetic resonance (1H NMR) spectroscopy techniques have been under study and, used concurrently to chemometric data management procedures, showed to be an interesting and promising tool for wine characterization according to both vintage and variety.

1H NMR chemometric models for classification of Czech wine type and variety

A set of 917 wines of Czech origin were analysed using nuclear magnetic resonance spectroscopy (NMR) with the aim of building and evaluating multivariate statistical models and machine learning methods for the classification of 6 types based on colour and residual sugar content, 13 wine grape varieties and 4 locations based on ¹H NMR spectra. The predictive models afforded greater than 93% correctness for classifying dry and medium dry, medium, and sweet white wines and dry red wines. The trained Random Forest (RF) model classified Pinot noir with 96% correctness, Blaufränkisch 96%, Riesling 92%, Cabernet Sauvignon 77%, Chardonnay 76%, Gewürtztraminer 60%, Hibernal 60%, Grüner Veltliner 52%, Pinot gris 48%, Sauvignon Blanc 45%, and Pálava 40%. Pinot blanc and Chardonnay, varieties that are often mistakenly interchanged, were discriminated with 71% correctness. The findings support chemometrics as a tool for predicting important features in wine, particularly for quality assessment and fraud detection.

13C NMR-Based Chemical Fingerprint for the Varietal and Geographical Discrimination of Wines

A fast, economic, and eco-friendly methodology for the wine variety and geographical origin differentiation using ¹³C nuclear magnetic resonance (NMR) data in combination with machine learning was developed. Wine samples of different grape varieties cultivated in different regions in Greece were subjected to ¹³C NMR analysis. The relative integrals of the ¹³C spectral window were processed and extracted to build a chemical fingerprint for the characterization of each specific wine variety, and then subjected to factor analysis, multivariate analysis of variance, and k-nearest neighbors analysis. The statistical analysis results showed that the ¹³C NMR fingerprint could be used as a rapid and accurate indicator of the wine variety differentiation. An almost perfect classification rate based on training (99.8%) and holdout methods (99.9%) was obtained. Results were further tested on the basis of Cronbach's alpha reliability analysis, where a very low random error (0.30) was estimated, indicating the accuracy and strength of the aforementioned methodology for the discrimination of the wine variety. The obtained data were grouped according to the geographical origin of wine samples and further subjected to principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA and variable importance in projection (VIP) allowed the determination of a chemical fingerprint characteristic of each geographical group. The statistical analysis revealed the possibility of acquiring useful information on wines, by simply processing the ¹³C NMR raw data, without the need to determine any specific metabolomic profile. In total, the obtained fingerprint can be used for the development of rapid quality-control methodologies concerning wine.

Yeast-Yeast Interactions: Mechanisms, Methodologies and Impact on Composition

During the winemaking process, alcoholic fermentation is carried out by a consortium of yeasts in which interactions occurs. The consequences of these interactions on the wine matrix have been widely described for several years with the aim of controlling the winemaking process as well as possible. In this review, we highlight the wide diversity of methodologies used to study these interactions, and their underlying mechanisms and consequences on the final wine composition and characteristics. The wide variety of matrix parameters, yeast couples, and culture conditions have led to contradictions between the results of the different studies considered. More recent aspects of modifications in the composition of the matrix are addressed through different approaches that have not been synthesized recently. Non-volatile and volatile metabolomics, as well as sensory analysis approaches are developed in this paper. The description of the matrix composition modification does not appear sufficient to explain interaction mechanisms, making it vital to take an integrated approach to draw definite conclusions on them.

Metabolomics Reveals Discrimination of Chinese Propolis from Different Climatic Regions

The chemical profiles of propolis vary greatly due to the botanic sources and geographic origins, which limit its standardization for modern usages. Here, we proposed a reliable ¹H NMR-based metabolomic approach, to discriminate the function and quality of Chinese propolis. A total 63 Chinese propolis samples from different temperate regions were collected and extracted for NMR analysis. Twenty-one compositions in ethanol extracts were assigned based on characteristic chemical shifts and previous literature reports. Significant geographic indicators were identified after the PCA and orthogonal partial least squares discriminant analysis (OPLS-DA) analysis of the obtained ¹H NMR data. It was found that the composition discriminations arose from long-term acclimation of the different climates of botanic origin and caused the differences in the biological activities. This study provides us a reasonable instruction for the quality control of Chinese propolis.

Analysis of metabolites in chardonnay dry white wine with various inactive yeasts by 1H NMR spectroscopy combined with pattern recognition analysis

The study aimed to investigate the effect of five inactive yeasts on the metabolites of Chardonnay dry white wines vinified in 2016 in Shacheng Manor Wine Co. Ltd., Hebei province, China. In this research, proton nuclear magnetic resonance (NMR) spectroscopy coupled multivariate analysis (¹H NMR-PCA/PLS-DA) were applied to identify and discriminate the different wine products. The results of principle component analysis (PCA) showed that there was significant difference between the metabolites of sample wines with different inactive yeasts, among them, the content of polyols, organic acids, amino acids and choline was notably influenced. The results of partial least squares discrimination analysis (PLS-DA) confirmed that the metabolites contributed to the discrimination of the wines were 2,3-butanediol, ethyl acetate, malic acid, valine, succinic acid, lactic acid, tartaric acid, glycerol, gallic acid, choline, proline, and alanine.

1H NMR metabolomics applied to Bordeaux red wines

The q-NMR metabolomics has already demonstrated its potential for classifying wines of different geographical origins, grape varieties, or vintages. This study focuses on the characterisation of Bordeaux red wines, seeking to discriminate them from others produced in the major French wine regions. A sampling of 224 commercial French wines was analysed by ¹H NMR and forty compounds were quantified. Non-supervised and supervised statistical analyses revealed a singular imprint of Bordeaux wines in comparison with other French wines, with classification rates ranging from 71% to 100%. Within the Bordeaux vineyards, red wines from the different Bordeaux subdivisions were analysed from different vintages. Our results indicate that q-NMR metabolomics enables the differentiation of Médoc and Libournais vineyard highlighting the most discriminant constituents. In addition, the effects of wine evolution during bottle aging and vintage on Bordeaux red wines were pointed out and discussed.

Wine and grape marc spirits metabolomics

INTRODUCTION

Mass spectrometry (MS)-based and nuclear magnetic resonance (NMR) spectroscopic analyses play a key role in the field of metabolomics due to their important advantages. The use of metabolomics in wine and grape marc spirits allows a more holistic perspective in monitoring and gaining information on the making processes and thus it can assist on the improvement of their quality.

OBJECTIVES

This review surveys the latest metabolomics approaches for wine and grape marc spirits with a focus on the description of MS-based and NMR spectroscopic analytical techniques.

METHODS

We reviewed the literature to identify metabolomic studies of wine and grape marc spirits that were published until the end of 2017, with the key term combinations of 'metabolomics', 'wine' and 'grape marc spirits'. Through the reference lists from these studies, additional articles were identified.

RESULTS

The results of this review showed that the application of different metabolomics approaches has significantly increased the knowledge of wine metabolome and grape marc spirits; however there is not yet a single analytical platform that can completely separate, detect and identify all metabolites in one analysis.

CONCLUSIONS

The authentication and quality control of wines and grape marc spirits has to be taken with caution, since the product's chemical composition could be affected by many factors. Despite intrinsic limitations, NMR spectroscopy and MS based strategies remain the key analytical methods in metabolomics studies. Authenticity, traceability and health issues related to their consumption are the major research initiatives in wine and grape marc spirits metabolomics analysis.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.

High-Resolution Magic-Angle-Spinning NMR and Magnetic Resonance Imaging Spectroscopies Distinguish Metabolome and Structural Properties of Maize Seeds from Plants Treated with Different Fertilizers and Arbuscular mycorrhizal fungi

Both high-resolution magic-angle-spinning (HRMAS) and magnetic resonance imaging (MRI) NMR spectroscopies were applied here to identify the changes of metabolome, morphology, and structural properties induced in seeds (caryopses) of maize plants grown at field level under either mineral or compost fertilization in combination with the inoculation by arbuscular mycorrhizal fungi (AMF). The metabolome of intact caryopses was examined by HRMAS-NMR, while the morphological aspects, endosperm properties and seed water distribution were investigated by MRI. Principal component analysis (PCA) was applied to evaluate ¹H CPMG (Carr-Purcel-Meiboom-Gill) HRMAS spectra as well as several MRI-derived parameters ( T₁, T₂, and self-diffusion coefficients) of intact maize caryopses. PCA score-plots from spectral results indicated that both seeds metabolome and structural properties depended on the specific field treatment undergone by maize plants. Our findings show that a combination of multivariate statistical analyses with advanced and nondestructive NMR techniques, such as HRMAS and MRI, enables the evaluation of the effects induced on maize caryopses by different fertilization and management practices at field level. The spectroscopic approach adopted here may become useful for the objective appraisal of the quality of seeds produced under a sustainable agriculture.

Recent applications of NMR in food and dietary studies

Over the last decade, a wide variety of new foods have been introduced into the global marketplace, many with health benefits that exceed those of traditional foods. Simultaneously, a wide range of analytical technologies has evolved that allow greater capability for the determination of food composition. Nuclear magnetic resonance (NMR), traditionally a research tool used for structural elucidation, is now being used frequently for metabolomics and chemical fingerprinting. Its stability and inherent ease of quantification have been exploited extensively to identify and quantify bioactive components in foods and dietary supplements. In addition, NMR fingerprints have been used to differentiate cultivars, evaluate sensory properties of food and investigate the influence of growing conditions on food crops. Here we review the latest applications of NMR in food analysis. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Determination of Alcohol Content in Alcoholic Beverages Using 45 MHz Benchtop NMR Spectrometer

Alcohol or ethanol is considered the most widely used recreational drug worldwide, and its production, consumption, and sale are strictly regulated by laws. Alcohol content of alcoholic beverages (wine, beers, and spirits) is about 3–50% v/v. Analytical methods to determine the alcohol content must be reliable, precise, and accurate. In this study, the amount of ethanol in several alcoholic beverages was determined using a 45 MHz low-field benchtop NMR (nuclear magnetic resonance) spectrometer. Internal standard and standard addition analytical methods were utilized to quantify ethanol. For both methods, acetic acid or acetonitrile was used as internal standard to quantify alcohol content by using the peak area corresponding to the methyl peaks of ethanol, acetic acid, or acetonitrile. Results showed that internal standard method gave values of percent alcohol that are in close agreement with the indicated label as confirmed by running the samples in a 400 MHz high-field NMR spectrometer using acetic acid as internal standard. This study demonstrates the utility of a benchtop NMR spectrometer that can provide an alternative technique to analyze percent alcohol in alcoholic products.

Viticultural practice and winemaking effects on metabolic profile of Negroamaro

Metabolic profiles of 32 Negroamaro red wines were analysed using (1)H NMR spectroscopy and multivariate statistical analyses (Principal Component Analysis, PCA, and Orthogonal Partial Least Square Discriminant Analysis, OPLS-DA). Among winemaking technologies three were compared: ultrasounds (U; 12 samples), cryomaceration using dry ice (C; 12 samples) and traditional (T; 8 samples). Moreover, each vinification technology was used for grapes grown by two different soil management practices, soil tillage (ST; 16 samples) and cover crop (CC; 16 samples), and by two different training systems, monolateral (M; 16 samples) and bilateral Guyot (B; 16 samples). All statistical models applied on NMR data revealed a good separation between ST (soil tillage) and CC (cover crop), showing a higher influence of the soil management practices compared to the winemaking technologies (ultrasound, cryomaceration and traditional). The differentiation among samples, due to soil management practices, was mainly caused by metabolites such as glycerol, 2,3-butanediol, malic acid, α/β-glucose and phenolic compounds, such as tyrosine and caffeic acid.