Sensory attributes of wine influenced by variety and berry shading discriminated by NMR metabolomics

Emerging trends and applications of metabolomics in food science and nutrition

The study of all chemical processes involving metabolites is known as metabolomics. It has been developed into an essential tool in several disciplines, such as the study of plant physiology, drug development, human diseases, and nutrition. The field of food science, diagnostic biomarker research, etiological analysis in the field of medical therapy, and raw material quality, processing, and safety have all benefited from the use of metabolomics recently. Food metabolomics includes the use of metabolomics in food production, processing, and human diets. As a result of changing consumer habits and the rising of food industries all over the world, there is a remarkable increase in interest in food quality and safety. It requires the employment of various technologies for the food supply chain, processing of food, and even plant breeding. This can be achieved by understanding the metabolome of food, including its biochemistry and composition. Additionally, Food metabolomics can be used to determine the similarities and differences across crop kinds, as an indicator for tracking the process of ripening to increase crops' shelf life and attractiveness, and identifying metabolites linked to pathways responsible for postharvest disorders. Moreover, nutritional metabolomics is used to investigate the connection between diet and human health through detection of certain biomarkers. This review assessed and compiled literature on food metabolomics research with an emphasis on metabolite extraction, detection, and data processing as well as its applications to the study of food nutrition, food-based illness, and phytochemical analysis. Several studies have been published on the applications of metabolomics in food but further research concerning the use of standard reproducible procedures must be done. The results published showed promising uses in the food industry in many areas such as food production, processing, and human diets. Finally, metabolome-wide association studies (MWASs) could also be a useful predictor to detect the connection between certain diseases and low molecular weight biomarkers.

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.

Mouthfeel subqualities in wines: A current insight on sensory descriptors and physical-chemical markers

Astringency and more generally mouthfeel perception are relevant to the overall quality of the wine. However, their origin and description are still uncertain and are constantly updating. Additionally, the terminology related to mouthfeel properties is expansive and extremely diversified, characterized by common traditional terms as well as novel recently adopted descriptors. In this context, this review evaluated the mention frequency of astringent subqualities and other mouthfeel attributes in the scientific literature of the last decades (2000-August 17, 2022). One hundred and twenty-five scientific publications have been selected and classified based on wine typology, aim, and instrumental-sensorial methods adopted. Dry resulted as the most frequent astringent subquality (10% for red wines, 8.6% for white wines), while body-and related terms-is a common mouthfeel sensation for different wine types, although its concept is still vague. Alongside, promising analytical and instrumental techniques investigating and simulating the in-mouth properties are discussed in detail, such as rheology for the viscosity and tribology for the lubrication loss, as well as the different approaches for the quantitative and qualitative evaluation of the interaction between salivary proteins and astringency markers. A focus on the phenolic compounds involved in the tactile perception was conducted, with tannins being the compounds conventionally found responsible for astringency. Nevertheless, other non-tannic polyphenolic classes (i.e., flavonols, phenolic acids, anthocyanins, anthocyanin-derivative pigments) as well as chemical-physical factors and the wine matrix (i.e., polysaccharides, mannoproteins, ethanol, glycerol, and pH) can also contribute to the wine in-mouth sensory profile. An overview of mouthfeel perception, factors involved, and its vocabulary is useful for enologists and consumers.

The importance of outlier rejection and significant explanatory variable selection for pinot noir wine soft sensor development

Sensory attributes are essential factors in determining the quality of wines. However, it can be challenging for consumers, even experts, to differentiate and quantify wines' sensory attributes for quality control. Soft sensors based on rapid chemical analysis offer a potential solution to overcome this challenge. However, the current limitation in developing soft sensors for wines is the need for a significant number of input parameters, at least 12, necessitating costly and time-consuming analyses. While such a comprehensive approach provides high accuracy in sensory quality mapping, the expensive and time-consuming studies required do not lend themselves to the industry's routine quality control activities. In this work, Box plots, Tucker-1 plots, and Principal Component Analysis (PCA) score plots were used to deal with output data (sensory attributes) to improve the model quality. More importantly, this work has identified that the number of analyses required to fully quantify by regression models and qualify by classification models can be significantly reduced. Based on regression models, only four key chemical parameters (total flavanols, total tannins, A_520nm^HCl, and pH) were required to accurately predict 35 sensory attributes of a wine with R² values above 0.6 simultaneously. In addition, for classification models to accurately predict 35 sensory attributes of a wine at once with prediction accuracy above 70%, only four key chemical parameters (A_280nm^HCl, A_520nm^HCl, chemical age and pH) were required. These models with reduced chemical parameters complement each other in sensory quality mapping and provide acceptable accuracy. The application of the soft sensor based on these reduced sets of key chemical parameters translated to a potential reduction in analytical cost and labour cost of 56% for the regression model and 83% for the classification model, respectively, making these models suitable for routine quality control use.

Untargeted Metabolomics Discriminates Grapes and Wines from Two Syrah Vineyards Located in the Same Wine Region

The influence of terroir in determining wine sensory properties is supported by the specific grape microbiome and metabolome, which provide distinct regional wine characteristics. In this work, the metabolic composition of grapes, must and wine of the Syrah grape variety cultivated on two sites in the same region was investigated. Concomitantly, a sensorial analysis of the produced wines was performed. Ultra-high-resolution liquid chromatography coupled with tandem mass spectrometry (UHPLC-Q-ToF-MS/MS) was applied to identify grape and wine metabolites. Untargeted metabolomics was used to identify putative biomarkers for terroir differentiation. More than 40 compounds were identified, including 28 phenolic compounds and 15 organic acids. The intensity evolution of the analyzed chemical compounds showed similar behavior during the fermentation process in both terroirs. However, the metabolic analysis of the grape, must and wine samples enabled the identification of an anthocyanin, chrysanthemin, as a putative biomarker of terroir 1. The overall sensorial quality of the wines was also evaluated, and according to the hitherto reported results, the wines from site 1 scored better than the wines from site 2. The results highlight the potential of metabolomics to assess grape and wine quality, as well as terroir association.

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.

Valorization of Traditional Alcoholic Beverages: The Study of the Sicilian Amarena Wine during Bottle Aging

Traditional alcoholic beverages have always been part of the Mediterranean culture and, lately, they have been re-evaluated to valorize both the territory and local customs. In this study, the Amarena wine, a fortified wine included in the national list of the traditional agri-food products, was characterized during bottle aging for oenological parameters, chromaticity, volatiles, and inorganic elements. Then, experimental data were visually interpreted by a principal component analysis (PCA). PCA revealed that most of oenological parameters (i.e., alcoholic grade, total dry extract, sugars, organic acids, and phenolic compounds) had a scarce discriminating power. Additionally, ethyl esters were only present in younger products, while remaining at quite constant levels. Conversely, certain metals (i.e., Mg, Na, Mn, Zn, and Cu), chromatic properties, and pH differentiated older Amarena bottles from the younger counterpart. Particularly, acetaldehyde and furanic compounds proved to be valid aging markers. A sensorial analysis highlighted that fruity and floral odors and flavors characterized younger beverages, while dried fruity, nutty, and spicy notes were displayed by older products, along with the valuable attribute of “oxidized” typically observed in aged Sherry wines. Overall, this study may encourage the production and commercialization of the Amarena wine, thus preserving the cultural heritage of the Mediterranean area.

Application of untargeted volatile profiling and data driven approaches in wine flavoromics research

Traditional flavor chemistry research usually makes use of targeted approaches by focusing on the detection and quantification of key flavor active metabolites that are present in food and beverages. In the last decade, flavoromics has emerged as an alternative to targeted methods where non-targeted and data driven approaches have been used to determine as many metabolites as possible with the aim to establish relationships among the chemical composition of foods and their sensory properties. Flavoromics has been successfully applied in wine research to gain more insights into the impact of a wide range of flavor active metabolites on wine quality. In this review, we aim to provide an overview of the applications of flavoromics approaches in wine research based on existing literature mainly by focusing on untargeted volatile profiling of wines and how this can be used as a powerful tool to generate novel insights. We highlight the fact that untargeted volatile profiling used in flavoromics approaches ultimately can assist the wine industry to produce different wine styles and to market existing wines appropriately based on consumer preference. In addition to summarizing the main steps involved in untargeted volatile profiling, we also provide an outlook about future perspectives and challenges of wine flavoromics research.

Quality evaluation of Cabernet Sauvignon wines in different vintages by 1H nuclear magnetic resonance-based metabolomics

A proton nuclear magnetic resonance (NMR)-based metabolomic study was used to characterize 2009, 2010, 2011, and 2012 vintages of Cabernet Sauvignon wines from Ningxia, which were vinified using the same fermentation technique. The pattern recognition methods of principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal PLS-DA (OPLS-DA) clearly distinguished between the different vintages of wine driven by the following metabolites: valine, 2,3-butanediol, ethyl acetate, proline, succinic acid, lactic acid, acetic acid, glycerol, gallic acid, and choline. The PLS-DA loading plots also differentiated among the metabolites of different vintages. In the 2009 vintage wines, we found the highest levels of gallic acid, valine, proline, and 2,3-butanediol. The 2011 vintage wines contained the highest levels of lactic acid, and the highest levels of ethyl acetate, succinic acid, glycerol, and choline were observed in the 2012 vintage wines. We selected eight metabolites from the 1H NMR spectra that were quantified according to their peak areas, and the concentrations were in agreement with the results of PLS-DA and OPLS-DA analyses.

NMR spectroscopy in wine authentication: An official control perspective

Wine authentication is vital in identifying malpractice and fraud, and various physical and chemical analytical techniques have been employed for this purpose. Besides wet chemistry, these include chromatography, isotopic ratio mass spectrometry, optical spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy, which have been applied in recent years in combination with chemometric approaches. For many years, ² H NMR spectroscopy was the method of choice and achieved official recognition in the detection of sugar addition to grape products. Recently, ¹ H NMR spectroscopy, a simpler and faster method (in terms of sample preparation), has gathered more and more attention in wine analysis, even if it still lacks official recognition. This technique makes targeted quantitative determination of wine ingredients and nontargeted detection of the metabolomic fingerprint of a wine sample possible. This review summarizes the possibilities and limitations of ¹ H NMR spectroscopy in analytical wine authentication, by reviewing its applications as reported in the literature. Examples of commercial and open-source solutions combining NMR spectroscopy and chemometrics are also examined herein, together with its opportunities of becoming an official method.

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.

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.

Non-Targeted Metabolite Profiles and Sensory Properties Elucidate Commonalities and Differences of Wines Made with the Same Variety but Different Cultivar Clones

Grapes, one of the oldest agricultural crops, are cultivated to produce table fruits, dried fruits, juice, and wine. Grapevine variety is composed of clones that share common morphological traits. However, they can differ in minor genetic mutations which often result in not only notorious morphological changes but also in other non-visible sensorial distinctive attributes. In the present work, we identified three Vitis vinifera cv. Pinot noir clones grown under identical field conditions that showed different grape cluster types. Here, sensorial analysis together with non-targeted metabolite profiles by Ultra High performance Liquid Chromatography (UPLC) couples to Ultra High Resolution Mass Spectrometry (FT-ICR-MS) of wines elaborated from the three different grape cluster types was studied with the aim of (i) finding sensorial differences among these three types of wines, and, if there were, (ii) determining the molecular features (metabolites) associated with these sensorial attributes by a multivariate statistical approach.

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.

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.

Wine Traceability Using Chemical Analysis, Isotopic Parameters, and Sensory Profiles

NMR/IRMS techniques are now widely used to assess the geographical origin of wines. The sensory profile of a wine is also an interesting method of characterizing its origin. This study aimed at elaborating chemical, isotopic, and sensory parameters by means of statistical analysis. The data were determined in some Italian white wines—Verdicchio and Fiano—and red wines—Refosco dal Peduncolo Rosso and Nero d’Avola—produced from grapes grown in two different regions with different soil and climatic conditions during the years 2009–2010. The grapes were cultivated in Veneto (northwest Italy) and Marches (central Italy). The results show that the multivariate statistical analysis PCA (Principal Component Analysis) of all the data can be a useful tool to characterize the vintage and identify the origin of wines produced from different varieties. Moreover, it could discriminate wines of the same variety produced in regions with different soil and climatic conditions.

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.

Wide target analysis of acylglycerols in miso (Japanese fermented soybean paste) by supercritical fluid chromatography coupled with triple quadrupole mass spectrometry and the analysis of the correlation between taste and both acylglycerols and free fatty acids

RATIONALE

The acylglycerols in miso have not been studied although it is known that they are important to the taste. In order to determine the fatty acid constituents in the acylglycerols and analyze them individually, multiple reaction monitoring (MRM) was performed utilizing a single platform, typically using both gas chromatography/mass spectrometry and liquid chromatography/mass spectrometry.

METHODS

Acylglycerols and fatty acids (FAs) in miso were extracted using the Bligh-Dyer method. Supercritical fluid chromatography (SFC) with a C30 column was conducted for separation, and mass spectrometric (MS) analysis was performed with electrospray ionization using a triple quadrupole mass spectrometer (QqQMS) in the MRM mode.

RESULTS

The detection of FAs from the hydrolysis of acylglycerols and individual acylglycerols was achieved using only an SFC/MS platform. From the quality control (QC) sample of miso, we determined the main FA constituents, and then performed wide target analysis using MRM. In total, 23 triacylglycerols, 10 diacylglycerols, two monoacylglycerols, and five FAs were annotated effectively. Furthermore, the important compounds related to taste were determined through the analysis using both the relative quantitative data of acylglycerols and FAs and the quantitative descriptive analysis data of miso.

CONCLUSIONS

A method for the determination of the FA constituents in acylglycerols after hydrolysis and the comprehensive analysis of acylglycerols and FAs using MRM with SFC/QqQMS was developed. Using the data from the comprehensive analysis of acylglycerols and quantitative descriptive data, the key compounds related to taste were investigated. This type of research on lipids and the taste of food is expected to progress hereafter. Copyright © 2017 John Wiley & Sons, Ltd.

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.

Antioxidants: Characterization, natural sources, extraction and analysis

Recently many review papers regarding antioxidants from different sources and different extraction and quantification procedures have been published. However none of them has all the information regarding antioxidants (chemistry, sources, extraction and quantification). This article tries to take a different perspective on antioxidants for the new researcher involved in this field. Antioxidants from fruit, vegetables and beverages play an important role in human health, for example preventing cancer and cardiovascular diseases, and lowering the incidence of different diseases. In this paper the main classes of antioxidants are presented: vitamins, carotenoids and polyphenols. Recently, many analytical methodologies involving diverse instrumental techniques have been developed for the extraction, separation, identification and quantification of these compounds. Antioxidants have been quantified by different researchers using one or more of these methods: in vivo, in vitro, electrochemical, chemiluminescent, electron spin resonance, chromatography, capillary electrophoresis, nuclear magnetic resonance, near infrared spectroscopy and mass spectrometry methods.

Quantitative metabolite profiling of edible onion species by NMR and HPLC-MS

Allium genus is a treasure trove of valuable bioactive compounds with potentially therapeutically important properties. This work utilises HPLC-MS and a constrained total-line-shape (CTLS) approach applied to (1)H NMR spectra to quantify metabolites present in onion species to reveal important inter-species differences. Extensive differences were detected between the sugar concentrations in onion species. Yellow onion contained the highest and red onion the lowest amounts of amino acids. The main flavonol-glucosides were quercetin 3,4'-diglucoside and quercetin 4'-glucoside. In general, the levels of flavonols were, higher in yellow onions than in red onions, and garlic and leek contained a lower amount of flavonols than the other Allium species. Our results highlight how (1)H NMR together with HPLC-MS can be useful in the quantification and the identification of the most abundant metabolites, representing an efficient means to pinpoint important functional food ingredients from Allium species.

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.

Assessment of constituents in Allium by multivariate data analysis, high-resolution α-glucosidase inhibition assay and HPLC-SPE-NMR

Bulbs and leaves of 35 Allium species and cultivars bought or collected in 2010-2012 were investigated with multivariate data analysis, high-resolution α-glucosidase inhibition assays and HPLC-HRMS-SPE-NMR with the aim of exploring the potential of Allium as a future functional food for management of type 2 diabetes. It was found that 30 out of 106 crude extracts showed more than 80% inhibition of the α-glucosidase enzyme at a concentration of 40mg/mL (dry sample) or 0.4g/mL (fresh sample). High-resolution α-glucosidase biochromatograms of these extracts allowed fast identification of three analytes with α-glucosidase inhibitory activity, and subsequent HPLC-HRMS-SPE-NMR experiments allowed identification of these as N-p-coumaroyloctopamine, N-p-coumaroyltyramine, and quercetin. The distribution of these three compounds was mapped for all samples by HPLC-ESI-HRMS. Unsupervised principal component analysis of samples from 2012 indicated that a major difference between fresh material and dried material is the increased amount of quercetin, a known α-glucosidase inhibitor.

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

We employed (1)H NMR spectroscopy to examine the molecular profile of a white "Fiano di Avellino" wine obtained through fermentation by either a commercial or a selected autochthonous Saccharomyces cerevisiae yeast starter. The latter was isolated from the same grape variety used in the wine-making process in order to strengthen the relationship between wine molecular quality and its geographical origin. (1)H NMR spectra, where water and ethanol signals were suppressed by a presaturated T1-edited NMR pulse sequence, allowed for definition of the metabolic content of the two differently treated wines. Elaboration of NMR spectral data by multivariate statistical analyses showed that the two different yeasts led to significant diversity in the wine metabolomes. Our results indicate that metabolomics by (1)H NMR spectroscopy combined with multivariate statistical analysis enables wine differentiation as a function of yeast species and other wine-making factors, thereby contributing to objectively relate wine quality to the terroir.

Metabolomics for measuring phytochemicals, and assessing human and animal responses to phytochemicals, in food science

Metabolomics, comprehensive metabolite analysis, is finding increasing application as a tool to measure and enable the manipulation of the phytochemical content of foods, to identify the measures of dietary intake, and to understand human and animal responses to phytochemicals in the diet. Recent applications of metabolomics directed toward understanding the role of phytochemicals in food and nutrition are reviewed.

NMR-based metabolomics in wine science

As metabolomics is becoming an emerging field of 'omics' research, NMR serves as one of the major analytical approaches of the decade in metabolomic study, producing information-rich, highly reliable and reproducible data set in non-targeted or global and multivariate statistical analysis. Recently, NMR is successfully being used to characterize wine and find an association of wine metabolite with environmental and fermentative factors in vineyard and making wine. This review describes important analytical features and recent applications in/of NMR-based metabolomics in wine science.

Quality of sour cherry juice of different clones and cultivars (Prunus cerasus L.) determined by a combined sensory and NMR spectroscopic approach

Juice was manufactured from seven different sour cherry clones/cultivars and evaluated by quantitative descriptive sensory analysis and (1)H NMR spectroscopy. The sensory evaluation showed a large variation in several sensory attributes between the sour cherry clones/cultivars, which could be divided into two groups on the basis of both the sensory data and the NMR spectroscopic data. These groups were closely related to the genetic background of the clones. Kelleris clones were distinctly different from Stevnsberry and Fanal clones. Hence, (1)H NMR spectroscopic data seem to correlate with sensory quality of different sour cherry clones. In addition, malic acid was the most important metabolite for modeling the two highly correlated sensory attributes sweetness and sourness, whereas the glucose content had a slight effect and the fructose content had no impact on sweetness/sourness. Other metabolites (ethyl acetate, asparagine, ethanol) could be correlated with sensory attributes; however, a direct causal connection could not be established.

NMR spectrometers as "magnetic tongues": prediction of sensory descriptors in canned tomatoes

The perception of odor and flavor of food is a complicated physiological and psychological process that cannot be explained by simple models. Quantitative descriptive analysis is a technique used to describe sensory features. Nevertheless, the availability of a number of instrumental techniques has opened up the possibility to calibrate the sensory perception. In this frame, we have tested the potentiality of nuclear magnetic resonance spectroscopy as a predictive tool to measure sensory descriptors. In particular, we have used an NMR metabolomic approach that allowed us to differentiate the analyzed samples based on their chemical composition. We were able to correlate the NMR metabolomic fingerprints recorded for canned tomato samples to the sensory descriptors bitterness, sweetness, sourness, saltiness, tomato and metal taste, redness, and density, suggesting that NMR might be a very useful tool for the characterization of sensory features of tomatoes.

Metabolomics: a second-generation platform for crop and food analysis

The combined factors of financial and food security, a rapidly increasing population and the associated requirement for food generated sustainably in a changing environment have brought food swiftly to the top of most government agendas. The consequence of this is that we need to produce more food at an equivalent or higher quality with lower inputs. These aims are achievable using conventional breeding, but not in the required timelines, and thus state-of-the-art genetic and analytical technologies are coming to the forefront. The concept of metabolomics, underpinned by mainstream (GC–MS, LC–MS, NMR) and specialist (MALDI-TOF-MS) analytical technologies addressing broad chemical (class) targets and dynamic ranges, offers significant potential to add significant value to crop and food science and deliver on future food demands. Metabolomics has now found a home in the food analytical toolbox with raw material quality and safety the major quality areas, although, as we will show, it is translating beyond this into food storage, shelf-life and post-harvest processing.

Metabolic characterization of Palatinate German white wines according to sensory attributes, varieties, and vintages using NMR spectroscopy and multivariate data analyses

(1)H NMR (nuclear magnetic resonance spectroscopy) has been used for metabolomic analysis of 'Riesling' and 'Mueller-Thurgau' white wines from the German Palatinate region. Diverse two-dimensional NMR techniques have been applied for the identification of metabolites, including phenolics. It is shown that sensory analysis correlates with NMR-based metabolic profiles of wine. (1)H NMR data in combination with multivariate data analysis methods, like principal component analysis (PCA), partial least squares projections to latent structures (PLS), and bidirectional orthogonal projections to latent structures (O2PLS) analysis, were employed in an attempt to identify the metabolites responsible for the taste of wine, using a non-targeted approach. The high quality wines were characterized by elevated levels of compounds like proline, 2,3-butanediol, malate, quercetin, and catechin. Characterization of wine based on type and vintage was also done using orthogonal projections to latent structures (OPLS) analysis. 'Riesling' wines were characterized by higher levels of catechin, caftarate, valine, proline, malate, and citrate whereas compounds like quercetin, resveratrol, gallate, leucine, threonine, succinate, and lactate, were found discriminating for 'Mueller-Thurgau'. The wines from 2006 vintage were dominated by leucine, phenylalanine, citrate, malate, and phenolics, while valine, proline, alanine, and succinate were predominantly present in the 2007 vintage. Based on these results, it can be postulated the NMR-based metabolomics offers an easy and comprehensive analysis of wine and in combination with multivariate data analyses can be used to investigate the source of the wines and to predict certain sensory aspects of wine.

Symbiodinium-invertebrate symbioses and the role of metabolomics

Symbioses play an important role within the marine environment. Among the most well known of these symbioses is that between coral and the photosynthetic dinoflagellate, Symbiodinium spp. Understanding the metabolic relationships between the host and the symbiont is of the utmost importance in order to gain insight into how this symbiosis may be disrupted due to environmental stressors. Here we summarize the metabolites related to nutritional roles, diel cycles and the common metabolites associated with the invertebrate-Symbiodinium relationship. We also review the more obscure metabolites and toxins that have been identified through natural products and biomarker research. Finally, we discuss the key role that metabolomics and functional genomics will play in understanding these important symbioses.