NMR-based metabolomics in wine science

Cutting without a Knife: A Slice-Selective 2D 1H-13C HSQC NMR Sequence for the Analysis of Inhomogeneous Samples

Nuclear magnetic resonance (NMR) is a powerful technique with applications ranging from small molecule structure elucidation to metabolomics studies of living organisms. Typically, solution-state NMR requires a homogeneous liquid, and the whole sample is analyzed as a single entity. While adequate for homogeneous samples, such an approach is limited if the composition varies as would be the case in samples that are naturally heterogeneous or layered. In complex samples such as living organisms, magnetic susceptibility distortions lead to broad 1H line shapes, and thus, the additional spectral dispersion afforded by 2D heteronuclear experiments is often required for metabolite discrimination. Here, a novel, slice-selective 2D, 1H-13C heteronuclear single quantum coherence (HSQC) sequence was developed that exclusively employs shaped pulses such that only spins in the desired volume are perturbed. In turn, this permits multiple volumes in the tube to be studied during a single relaxation delay, increasing sensitivity and throughput. The approach is first demonstrated on standards and then used to isolate specific sample/sensor elements from a microcoil array and finally study slices within a living earthworm, allowing metabolite changes to be discerned with feeding. Overall, slice-selective NMR is demonstrated to have significant potential for the study of layered and other inhomogeneous samples of varying complexity. In particular, its ability to select subelements is an important step toward developing microcoil receive-only arrays to study environmental toxicity in tiny eggs, cells, and neonates, whereas localization in larger living species could help better correlate toxin-induced biochemical responses to the physical localities or organs involved.

Study of the Stability of Wine Samples for 1H-NMR Metabolomic Profile Analysis through Chemometrics Methods

Wine is a temperature, light, and oxygen-sensitive product, so its physicochemical characteristics can be modified by variations in temperature and time when samples are either sampled, transported, and/or analyzed. These changes can alter its metabolomic fingerprinting, impacting further classification tasks and quality/quantitative analyses. For these reasons, the aim of this work is to compare and analyze the information obtained by different chemometric methods used in a complementary form (PCA, ASCA, and PARAFAC) to study 1H-NMR spectra variations of four red wine samples kept at different temperatures and time lapses. In conjunction, distinctive changes in the spectra are satisfactorily tracked with each chemometric method. The chemometric analyses reveal variations related to the wine sample, temperature, and time, as well as the interactions among these factors. Moreover, the magnitude and statistical significance of the effects are satisfactorily accounted for by ASCA, while the time-related effects variations are encountered by PARAFAC modeling. Acetaldehyde, formic acid, polyphenols, carbohydrates, lactic acid, ethyl lactate, methanol, choline, succinic acid, proline, acetoin, acetic acid, 1,3-propanediol, isopentanol, and some amino acids are identified as some of the metabolites which present the most important variations.

Effects of Water Stress, Defoliation and Crop Thinning on Vitis vinifera L. cv. Solaris Must and Wine Part II: 1H NMR Metabolomics

Proton nuclear magnetic resonance (1H NMR) metabolomics was employed to investigate the impact of water deficit, defoliation, and crop thinning on the chemical composition of must and wines from the cool-climate white grape variety Solaris. The obtained results show that viticultural practices (defoliation and crop thinning) affected the amino acid and sugar content of Solaris must and thereby the quality of the final wine—mainly in terms of compounds normally related to fruity aroma (i.e., isopentanol), non-sugar sweetness (i.e., proline and glycerol), and alcohol content. The content of tyrosol, a natural phenolic antioxidant with a high bioavailability, was increased in the final wine by a combination of defoliation and crop thinning. The results of the metabolomics analysis performed on the must and wine samples from the water stress experiment showed that short-term water deficit significantly affected the concentration of several flavor-related compounds, including glutamate, butyrate and propanol, of the organic acids lactate and fumarate, and of the phenolic compounds caffeic acid and p-coumaric acid. ANOVA simultaneous component analysis showed that the effect of water deficit accounted for 11% (p < 0.001) and 8% (p < 0.001) of the variability in the metabolite concentrations in must and wines, respectively, while viticultural practices accounted for 38% (p < 0.001) and 30% (p < 0.001) of the metabolite variability in must and wines, respectively.

Monitoring of the Rioja red wine production process by 1 H-NMR spectroscopy

BACKGROUND

As an inherently quantitative and unbiased analytical technique, proton nuclear magnetic resonance (¹ H-NMR) provides an excellent method to monitor the quality of food and beverages, and a sensitive and informative tool to study the winemaking process.

RESULTS

By using NMR, it is possible to monitor quantitative changes in wine metabolites (amino acids, organic acids and some phenolic compounds) during the winemaking process, including wine ageing. This study shows an increase in the concentration of the phenols at the beginning of alcoholic fermentation, as well as a stabilization and slight increase in gallic acid and a slight decrease in resveratrol during the oak barrel ageing step.

CONCLUSION

This study demonstrates the potential of NMR as a process analytical technology (PAT) tool in the wine industry, by monitoring amino acids, organic acids and three polyphenols - gallic acid, catechin and resveratrol - during the winemaking process. This study of the time course evolution of wine has been conducted in a commercial winery rather than an experimental laboratory, demonstrating the capacity of this technique in commercial winemaking production. © 2021 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Chemical Composition and Polyphenolic Compounds of Red Wines: Their Antioxidant Activities and Effects on Human Health—A Review

Red wine, an alcoholic beverage is composed of a spectrum of complex compounds such as water, alcohol, glycerol, organic acid, carbohydrates, polyphenols, and minerals as well as volatile compounds. Major factors that affect the levels of phenolic compounds in red wines are the variety of grapes and the storage of the wines. Among the constituents of red wine, phenolic compounds play a crucial role in attributes including color and mouthfeel and confer beneficial properties on health. Most importantly, phenolic compounds such as flavanols, flavonols, flavanones, flavones, tannins, anthocyanins, hydroxycinnamic acids, hydroxybenzoic acids, and resveratrol can prevent the development of cardiovascular diseases, cancers, diabetes, inflammation, and some other chronic diseases.

NMR-based metabolomics approach on optimization of malolactic fermentation of sea buckthorn juice with Lactiplantibacillus plantarum

This work investigated the impact of malolactic fermentation on the metabolomic profile of sea buckthorn juice to optimize the fermentation process for flavor modification. Six strains of L. plantarum were used with varied pH of the juice, cell acclimation, and fermentation time. ¹H-NOESY spectra were acquired from fresh and fermented juices with a total of 46 metabolites identified. Less sugars and quinic acid were metabolized at pH 2.7 while oxidation of ascorbic acid was reduced at pH 3.5. l-Malic acid, essential amino acids, and nucleosides were consumed early during fermentation while sugars in general were consumed later in the fermentation. If deacidification is the main target of fermentation, strains that produce less acids and ferment less sugars, shorter fermentation time, and lower starter pH should be used. Higher starter pH and longer fermentation time promote formation of antimicrobial compounds and potentially increase antioxidant stability.

Comprehensive Classification and Regression Modeling of Wine Samples Using 1H NMR Spectra

Recently, ¹H NMR (nuclear magnetic resonance) spectroscopy was presented as a viable option for the quality assurance of foods and beverages, such as wine products. Here, a complex chemometric analysis of red and white wine samples was carried out based on their ¹H NMR spectra. Extreme gradient boosting (XGBoost) machine learning algorithm was applied for the wine variety classification with an iterative double cross-validation loop, developed during the present work. In the case of red wines, Cabernet Franc, Merlot and Blue Frankish samples were successfully classified. Three very common white wine varieties were selected and classified: Chardonnay, Sauvignon Blanc and Riesling. The models were robust and were validated against overfitting with iterative randomization tests. Moreover, four novel partial least-squares (PLS) regression models were constructed to predict the major quantitative parameters of the wines: density, total alcohol, total sugar and total SO₂ concentrations. All the models performed successfully, with R² values above 0.80 in almost every case, providing additional information about the wine samples for the quality control of the products. ¹H NMR spectra combined with chemometric modeling can be a good and reliable candidate for the replacement of the time-consuming traditional standards, not just in wine analysis, but also in other aspects of food science.

Compounds with Antiviral, Anti-Inflammatory and Anticancer Activity Identified in Wine from Hungary's Tokaj Region via High Resolution Mass Spectrometry and Bioinformatics Analyses

(1) Background: Wine contains a variety of molecules with potential beneficial effects on human health. Our aim was to examine the wine components with high-resolution mass spectrometry including high-resolution tandem mass spectrometry in two wine types made from grapes with or without the fungus Botrytis cinerea, or "noble rot". (2) For LC-MS/MS analysis, 12 wine samples (7 without and 5 with noble rotting) from 4 different wineries were used and wine components were identified and quantified. (3) Results: 288 molecules were identified in the wines and the amount of 169 molecules was statistically significantly different between the two wine types. A database search was carried out to find the molecules, which were examined in functional studies so far, with high emphasis on molecules with antiviral, anti-inflammatory and anticancer activities. (4) Conclusions: A comprehensive functional dataset related to identified wine components is also provided highlighting the importance of components with potential health benefits.

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.

New approach for wine authenticity screening by a cumulative 1H and 2H qNMR

New methodological approach for rapid control of wine authenticity without sample preparation, based on the quantitative NMR spectroscopy (qNMR) of the protium 1H and deuterium 2H nucleus is suggested. The content of dominant (e.g. water, ethanol) and some minor (e.g. glycerol, organic acids) components of wine are determined from qNMR 1H spectra for authentication of molecular composition. The sum of all exchanging hydrogen atoms of wine's components provide the 1H signal with a chemical shift of 4.8 ppm. Accounting for their content from 1H spectrum allows us calculate the 2H isotopic content in wine water from integral intensity of corresponding signal in the 2H qNMR spectrum using an internal or external standard with a known content of the 2H isotope. The possible addition of water can be found from comparison of this value with values of surface and/or ground waters from corresponding viticulture areas. This approach was used for white and red wines from the Black Sea region (Krasnodar area & Crimea peninsula). The 2H contents in investigated wines range from 157 to 165 ppm. The maximum 2H isotope content in surface waters does not exceed 148 ppm. A qNMR measurement of wine according to the proposed approach takes some minutes, that significantly exceeds the laboriousness of methods based on IRMS/SIRA (e.g. δ13C, δ18O). The error of qNMR measurements is less than 2.0%. The qNMR screening of deuterium (2H) in ethanol can be used for detection of possible wine chaptalization. This approach is similar to the known SNIF-NMR method. The positive difference with this method is the use of minimal quantity of enriched 2H standard and measurement of integral intensities of all signals instead of heights. It allows to reduce measurements' time as well as to measure the 2H content of all fragments of ethanol molecules – CH3−, CH2−, OH-groups. The publication has been prepared with the support of the “RUDN University Program 5–100”.

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.

Nontargeted Detection Methods for Food Safety and Integrity

Nontargeted workflows for chemical hazard analyses are highly desirable in the food safety and integrity fields to ensure human health. Two different analytical strategies, nontargeted metabolomics and chemical database filtering, can be used to screen unknown contaminants in food matrices. Sufficient mass and chromatographic resolutions are necessary for the detection of compounds and subsequent componentization and interpretation of candidate ions. Analytical chemistry–based technologies, including gas chromatography–mass spectrometry (GC-MS), liquid chromatography–mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and capillary electrophoresis–mass spectrometry (CE-MS), combined with chemometrics analysis are being used to generate molecular formulas of compounds of interest. The construction of a chemical database plays a crucial role in nontargeted detection. This review provides an overview of the current sample preparation, analytical chemistry–based techniques, and data analysis as well as the limitations and challenges of nontargeted detection methods for analyzing complex food matrices. Improvements in sample preparation and analytical platforms may enhance the relevance of food authenticity, quality, and safety.

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.

Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches

Chemical analysis of grape juice and wine has been performed for over 50 years in a targeted manner to determine a limited number of compounds using Gas Chromatography, Mass-Spectrometry (GC-MS) and High Pressure Liquid Chromatography (HPLC). Therefore, it only allowed the determination of metabolites that are present in high concentration, including major sugars, amino acids and some important carboxylic acids. Thus, the roles of many significant but less concentrated metabolites during wine making process are still not known. This is where metabolomics shows its enormous potential, mainly because of its capability in analyzing over 1000 metabolites in a single run due to the recent advancements of high resolution and sensitive analytical instruments. Metabolomics has predominantly been adopted by many wine scientists as a hypothesis-generating tool in an unbiased and non-targeted way to address various issues, including characterization of geographical origin (terroir) and wine yeast metabolic traits, determination of biomarkers for aroma compounds, and the monitoring of growth developments of grape vines and grapes. The aim of this review is to explore the published literature that made use of both targeted and untargeted metabolomics to study grapes and wines and also the fermentation process. In addition, insights are also provided into many other possible avenues where metabolomics shows tremendous potential as a question-driven approach in grape and wine research.

High-resolution mass spectrometry metabolomics of grape chemical markers to reveal use of not-allowed varieties in the production of Amarone and Recioto wines

INTRODUCTION

Grape varieties allowed to produce Amarone della Valpolicella and Recioto DOCG wines are strictly regulated by their disciplinary of production. These are Corvina Veronese and Corvinone grapes, to a lesser extent also Rondinella can be used. The use of other varieties, is not allowed.

OBJECTIVES

To identify chemical markers suitable to reveal addition of two not allowed grape varieties to the Corvina/Corvinone blend, such as Primitivo or Negro Amaro.

METHODS

The identification of the secondary metabolites of the four grape varieties was conducted by high-resolution mass spectrometry (HRMS) metabolomics. By using the signals of these metabolites the indexes able to identify the presence of Primitivo or Negro Amaro grapes in the Corvina/Corvinone 1:1 blend were calculated.

RESULTS

Indexes of laricitrin (Lr), delphinidin (Dp), and petunidin (Pt) signals were effective to identify the use of 10% Primitivo, while α-terpineol pentosyl-hexoside and linalool pentosyl-hexoside reveal the presence of Negro Amaro in the grape blend.

CONCLUSIONS

Varietal markers useful to detect the presence of Primitivo and Negro Amaro in the grape blend were identified by HRMS metabolomics, a method suitable to check the identity of grapes on arrival at the winery, as well as the fermenting musts. The effectiveness of the identified markers in the final wines have to be confirmed. Potentially, a similar approach can be used to reveal analogous frauds performed on other high-quality wines.

Wine evolution during bottle aging, studied by 1H NMR spectroscopy and multivariate statistical analysis

The study of wine evolution during bottle aging is an important aspect of wine quality. Ten different red wines (Vitis vinifera) from Piedmont region were analysed 3 months after bottling and after a further 48 month conservation in a climate controlled wine cellar kept at a constant/controlled temperature of 12 °C. Two white wines (Vitis vinifera) were included in this study for comparison purposes. White wines were analysed 3 months after bottling and after further 24 months of bottle aging in the same climate controlled wine cellar. Metabolite changes during this period were evaluated using ¹H NMR spectroscopy combined with statistical analysis. Metabolite variations due to wine aging were minimal compared to those that resulted from a different wine type and wine geographical origin. Therefore, it was necessary to remove this source of variability to discriminate between fresh and refined samples. The storage at low and controlled temperature for 2 or 4 years permitted a slow but progressive evolution of all wines under investigation. 1H NMR spectroscopy, implemented with statistical data analysis, allowed identifying and differentiating wine samples from the two aging stages. In most wines, a decrease in organic acids (lactic acid, succinic acid and tartaric acid) and an increase in esters (ethyl acetate and ethyl lactate) was observed. Catechin and epicatechin decreased during aging in all wines while gallic acid increased in almost all red wines.

Analysis of Metabolites in Cabernet Sauvignon and Shiraz Dry Red Wines from Shanxi by 1H NMR Spectroscopy Combined with Pattern Recognition Analysis

Metabolomics technology based on proton nuclear magnetic resonance (1H NMR) spectroscopy combined with pattern recognition analysis was used to characterize the Cabernet Sauvignon and Shiraz dry red wines vinified in the Linfen of Shanxi Province, China, in 2016. The results showed that there was a very significant difference between the metabolites of Cabernet Sauvignon and Shiraz dry red wines from the area of Linfen. Compared with Shiraz dry red wines, Cabernet Sauvignon dry red wines contained higher levels of proline, valine, tartaric acid, citric acid, malic acid, gallic acid, β-glucose and ethyl acetate, whereas 2,3-butanediol, lactic acid, choline, glycerol, α-D-glucuronic acid, succinic acid and alanine were present in lower levels. Application of NMR spectroscopy combined with pattern recognition analysis showed the discriminative power between wine varietals from the same production area. The loading plot from partial least squares discriminant analysis (PLs-DA) indicated that the key biomarkers for this differentiation were proline, tartaric acid, glycerol, lactic acid, choline, succinic acid and gallic acid, which was consistent with the result of quantitative analysis.

Cool-Climate Red Wines-Chemical Composition and Comparison of Two Protocols for ¹H-NMR Analysis

This study investigates the metabolome of 26 experimental cool-climate wines made from 22 grape varieties using two different protocols for wine analysis by proton nuclear magnetic resonance (¹H-NMR) spectroscopy. The wine samples were analyzed as-is (wet) and as dried samples. The NMR datasets were preprocessed by alignment and mean centering. No normalization or scaling was performed. The “wet” method preserved the inherent properties of the samples and provided a fast and effective overview of the molecular composition of the wines. The “dried” method yielded a slightly better sensitivity towards a broader range of the compounds present in wines. A total of 27 metabolites including amino acids, organic acids, sugars, and alkaloids were identified in the ¹H-NMR spectra of the wine samples. Principal component analysis was performed on both NMR datasets evidencing well-defined molecular fingerprints for ‘Baco Noir’, ‘Bolero’, ‘Cabernet Cantor’, ‘Cabernet Cortis’, ‘Don Muscat’, ‘Eszter’, ‘Golubok’, ‘New York Muscat’, ‘Regent’, ‘Rondo’, ‘Triomphe d’Alsace’, ‘Précose Noir’, and ‘Vinoslivy’ wines. Amongst the identified metabolites, lactic acid, succinic acid, acetic acid, gallic acid, glycerol, and methanol were found to drive sample groupings. The ¹H-NMR data was compared to the absolute concentration values obtained from a reference Fourier transform infrared method, evidencing a high correlation.

Quantitation of Minor Components in Mango Juice with Band-Selective Excitation NMR Spectroscopy

¹H NMR-based metabolic analysis of foods has been widely applied. However, dynamic range problems frequently impede its application because foodstuffs are composed of various organic compounds in a wide range of concentrations. Band-selective excitation ¹H NMR spectroscopy has been found to be a useful tool for observing the minor components in foods. Because quantitative information is important for metabolic analysis of foods and complete metabolome data, quantitation with the band-selective excitation ¹H NMR method was carefully investigated in the present study. As a result, the concentrations of minor components in mango juice of the "Carabao" cultivar were successfully quantitated by band-selective excitation ¹H NMR using standard curves that exhibited good linearity. The band-selective excitation ¹H NMR technique was therefore effective for determining the concentrations of minor components in foods.

Rapid discrimination of strain-dependent fermentation characteristics among Lactobacillus strains by NMR-based metabolomics of fermented vegetable juice

In this study, we investigated the applicability of NMR-based metabolomics to discriminate strain-dependent fermentation characteristics of lactic acid bacteria (LAB), which are important microorganisms for fermented food production. To evaluate the discrimination capability, six type strains of Lactobacillus species and six additional L. brevis strains were used focusing on i) the difference between homo- and hetero-lactic fermentative species and ii) strain-dependent characteristics within L. brevis. Based on the differences in the metabolite profiles of fermented vegetable juices, non-targeted principal component analysis (PCA) clearly separated the samples into those inoculated with homo- and hetero-lactic fermentative species. The separation was primarily explained by the different levels of dominant metabolites (lactic acid, acetic acid, ethanol, and mannitol). Orthogonal partial least squares discrimination analysis, based on a regions-of-interest (ROIs) approach, revealed the contribution of low-abundance metabolites: acetoin, phenyllactic acid, p-hydroxyphenyllactic acid, glycerophosphocholine, and succinic acid for homolactic fermentation; and ornithine, tyramine, and γ-aminobutyric acid (GABA) for heterolactic fermentation. Furthermore, ROIs-based PCA of seven L. brevis strains separated their strain-dependent fermentation characteristics primarily based on their ability to utilize sucrose and citric acid, and convert glutamic acid and tyrosine into GABA and tyramine, respectively. In conclusion, NMR metabolomics successfully discriminated the fermentation characteristics of the tested strains and provided further information on metabolites responsible for these characteristics, which may impact the taste, aroma, and functional properties of fermented foods.

A Concise Review on Multi-Omics Data Integration for Terroir Analysis in Vitis vinifera

Vitis vinifera (grapevine) is one of the most important fruit crops, both for fresh consumption and wine and spirit production. The term terroir is frequently used in viticulture and the wine industry to relate wine sensory attributes to its geographic origin. Although, it can be cultivated in a wide range of environments, differences in growing conditions have a significant impact on fruit traits that ultimately affect wine quality. Understanding how fruit quality and yield are controlled at a molecular level in grapevine in response to environmental cues has been a major driver of research. Advances in the area of genomics, epigenomics, transcriptomics, proteomics and metabolomics, have significantly increased our knowledge on the abiotic regulation of yield and quality in many crop species, including V. vinifera. The integrated analysis of multiple 'omics' can give us the opportunity to better understand how plants modulate their response to different environments. However, 'omics' technologies provide a large amount of biological data and its interpretation is not always straightforward, especially when different 'omic' results are combined. Here we examine the current strategies used to integrate multi-omics, and how these have been used in V. vinifera. In addition, we also discuss the importance of including epigenomics data when integrating omics data as epigenetic mechanisms could play a major role as an intermediary between the environment and the genome.

FoodPro: A Web-Based Tool for Evaluating Covariance and Correlation NMR Spectra Associated with Food Processes

Foods from agriculture and fishery products are processed using various technologies. Molecular mixture analysis during food processing has the potential to help us understand the molecular mechanisms involved, thus enabling better cooking of the analyzed foods. To date, there has been no web-based tool focusing on accumulating Nuclear Magnetic Resonance (NMR) spectra from various types of food processing. Therefore, we have developed a novel web-based tool, FoodPro, that includes a food NMR spectrum database and computes covariance and correlation spectra to tasting and hardness. As a result, FoodPro has accumulated 236 aqueous (extracted in D₂O) and 131 hydrophobic (extracted in CDCl₃) experimental bench-top 60-MHz NMR spectra, 1753 tastings scored by volunteers, and 139 hardness measurements recorded by a penetrometer, all placed into a core database. The database content was roughly classified into fish and vegetable groups from the viewpoint of different spectrum patterns. FoodPro can query a user food NMR spectrum, search similar NMR spectra with a specified similarity threshold, and then compute estimated tasting and hardness, covariance, and correlation spectra to tasting and hardness. Querying fish spectra exemplified specific covariance spectra to tasting and hardness, giving positive covariance for tasting at 1.31 ppm for lactate and 3.47 ppm for glucose and a positive covariance for hardness at 3.26 ppm for trimethylamine N-oxide.

Holistic Analysis Enhances the Description of Metabolic Complexity in Dietary Natural Products

In the field of food and nutrition, complex natural products (NPs) are typically obtained from cells/tissues of diverse organisms such as plants, mushrooms, and animals. Among them, edible fruits, grains, and vegetables represent most of the human diet. Because of an important dietary dependence, the comprehensive metabolomic analysis of dietary NPs, performed holistically via the assessment of as many metabolites as possible, constitutes a fundamental building block for understanding the human diet. Both mass spectrometry (MS) and nuclear magnetic resonance (NMR) are important complementary analytic techniques, covering a wide range of metabolites at different concentrations. Particularly, 1-dimensional 1H-NMR offers an unbiased overview of all metabolites present in a sample without prior knowledge of its composition, thereby leading to an untargeted analysis. In the past decade, NMR-based metabolomics in plant and food analyses has evolved considerably. The scope of the present review, covering literature of the past 5 y, is to address the relevance of 1H-NMR–based metabolomics in food plant studies, including a comparison with MS-based techniques. Major applications of NMR-based metabolomics for the quality control of dietary NPs and assessment of their nutritional values are presented.

Proton Nuclear Magnetic Resonance-Spectroscopic Discrimination of Wines Reflects Genetic Homology of Several Different Grape (V. vinifera L.) Cultivars

Background and Aims

Proton nuclear magnetic resonance spectroscopy coupled multivariate analysis (¹H NMR-PCA/PLS-DA) is an important tool for the discrimination of wine products. Although ¹H NMR has been shown to discriminate wines of different cultivars, a grape genetic component of the discrimination has been inferred only from discrimination of cultivars of undefined genetic homology and in the presence of many confounding environmental factors. We aimed to confirm the influence of grape genotypes in the absence of those factors.

Methods and Results

We applied ¹H NMR-PCA/PLS-DA and hierarchical cluster analysis (HCA) to wines from five, variously genetically-related grapevine (V. vinifera) cultivars; all grown similarly on the same site and vinified similarly. We also compared the semi-quantitative profiles of the discriminant metabolites of each cultivar with previously reported chemical analyses. The cultivars were clearly distinguishable and there was a general correlation between their grouping and their genetic homology as revealed by recent genomic studies. Between cultivars, the relative amounts of several of the cultivar-related discriminant metabolites conformed closely with reported chemical analyses.

Conclusions

Differences in grape-derived metabolites associated with genetic differences alone are a major source of ¹H NMR-based discrimination of wines and ¹H NMR has the capacity to discriminate between very closely related cultivars.

Significance of the Study

The study confirms that genetic variation among grape cultivars alone can account for the discrimination of wine by ¹H NMR-PCA/PLS and indicates that ¹H NMR spectra of wine of single grape cultivars may in future be used in tandem with hierarchical cluster analysis to elucidate genetic lineages and metabolomic relations of grapevine cultivars. In the absence of genetic information, for example, where predecessor varieties are no longer extant, this may be a particularly useful approach.

Metabolomic unveiling of a diverse range of green tea (Camellia sinensis) metabolites dependent on geography

Numerous factors such as geographical origin, cultivar, climate, cultural practices, and manufacturing processes influence the chemical compositions of tea, in the same way as growing conditions and grape variety affect wine quality. However, the relationships between these factors and tea chemical compositions are not well understood. In this study, a new approach for non-targeted or global analysis, i.e., metabolomics, which is highly reproducible and statistically effective in analysing a diverse range of compounds, was used to better understand the metabolome of Camellia sinensis and determine the influence of environmental factors, including geography, climate, and cultural practices, on tea-making. We found a strong correlation between environmental factors and the metabolome of green, white, and oolong teas from China, Japan, and South Korea. In particular, multivariate statistical analysis revealed strong inter-country and inter-city relationships in the levels of theanine and catechin derivatives found in green and white teas. This information might be useful for assessing tea quality or producing distinct tea products across different locations, and highlights simultaneous identification of diverse tea metabolites through an NMR-based metabolomics approach.

High precision mass measurements for wine metabolomics

An overview of the critical steps for the non-targeted Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) analysis of wine chemistry is given, ranging from the study design, data preprocessing and statistical analyses, to markers identification. UPLC-Q-ToF-MS data was enhanced by the alignment of exact mass data from FTICR-MS, and marker peaks were identified using UPLC-Q-ToF-MS(2). In combination with multivariate statistical tools and the annotation of peaks with metabolites from relevant databases, this analytical process provides a fine description of the chemical complexity of wines, as exemplified in the case of red (Pinot noir) and white (Chardonnay) wines from various geographic origins in Burgundy.

Broadband WET: a novel technique for quantitative characterization of minor components in foods

NMR analysis of foods frequently suffers from a problem of dynamic range, which limits the detection of minor components due to the huge signals of water and major components such as sugars. In the present study, we propose a new method named as 'broadband WET'. This pulse scheme was applied to persimmon fruit juice for saturating the resonances of water and sugars, which covered a broad bandwidth. In comparison with the conventional solvent suppression methods such as WET and DPFGSE-WATERGATE, it was shown that broadband WET provided highly selective suppression of resonances covering an extensive bandwidth and quantitative signals of minor components without distortion. The proposed method is suitable to detect quantitative signals of the minor components with a high sensitivity.

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.

Experiments for the editing of singlet peaks and simplification of ¹H NMR spectra of complex mixtures

A 1D NMR singlet filtered experiment (s-filter) is presented for the clean editing of singlets peaks in crowded ¹H NMR spectra. The experiment affords a clean editing of singlet peaks and weak coupled signals ((n)JHH < 3 Hz), while the remaining multiplets peaks are strongly attenuated to a factor over 90% of its original intensity. The experiment provided a useful simplification of the typically congested ¹H NMR spectra of foodstuffs, and the edited signals retained most of the sensitivity and quantitativity. The versatility of the s-filter scheme permitted its easy incorporation as excitation building block into other 1D and 2D experiments, providing a further editing of the singlet responses. The combination of the 1D s-filter experiment and difference spectroscopy provided a 1D reverse singlet filtered experiment (s-filter(rev)) for the clean editing of multiplet peaks and strong suppression of singlets. The experiments proposed could be valuable to facilitate the detection and/or quantification of target compounds in complex mixtures of metabolites.

Factor analysis of 27Al MAS NMR spectra for identifying nanocrystalline phases in amorphous geopolymers

Nanostructured materials offer enhanced physicochemical properties because of the large interfacial area. Typically, geopolymers with specifically synthesized nanosized zeolites are a promising material for the sorption of pollutants. The structural characterization of these aluminosilicates, however, continues to be a challenge. To circumvent complications resulting from the amorphous character of the aluminosilicate matrix and from the low concentrations of nanosized crystallites, we have proposed a procedure based on factor analysis of (27)Al MAS NMR spectra. The capability of the proposed method was tested on geopolymers that exhibited various tendencies to crystallize (i) completely amorphous systems, (ii) X-ray amorphous systems with nanocrystalline phases, and (iii) highly crystalline systems. Although the recorded (27)Al MAS NMR spectra did not show visible differences between the amorphous systems (i) and the geopolymers with the nanocrystalline phase (ii), the applied factor analysis unambiguously distinguished these materials. The samples were separated into the well-defined clusters, and the systems with the evolving crystalline phase were identified even before any crystalline fraction was detected by X-ray powder diffraction. Reliability of the proposed procedure was verified by comparing it with (29)Si MAS NMR spectra. Factor analysis of (27)Al MAS NMR spectra thus has the ability to reveal spectroscopic features corresponding to the nanocrystalline phases. Because the measurement time of (27)Al MAS NMR spectra is significantly shorter than that of (29)Si MAS NMR data, the proposed procedure is particularly suitable for the analysis of large sets of specifically synthesized geopolymers in which the formation of the limited fractions of nanocrystalline phases is desired.

Foodomics: a new comprehensive approach to food and nutrition

In the past 20 years, the scientific community has faced a great development in different fields due to the development of high-throughput, omics technologies. Starting from the four major types of omics measurements (genomics, transcriptomics, proteomics, and metabolomics), a variety of omics subdisciplines (epigenomics, lipidomics, interactomics, metallomics, diseasomics, etc.) has emerged. Thanks to the omics approach, researchers are now facing the possibility of connecting food components, foods, the diet, the individual, the health, and the diseases, but this broad vision needs not only the application of advanced technologies, but mainly the ability of looking at the problem with a different approach, a "foodomics approach". Foodomics is the comprehensive, high-throughput approach for the exploitation of food science in the light of an improvement of human nutrition. Foodomics is a new approach to food and nutrition that studies the food domain as a whole with the nutrition domain to reach the main objective, the optimization of human health and well-being.