NMR in the Service of Wine Differentiation

1H NMR Spectroscopy Primitivo Red Wine Screening After Grape Pomace Repassage for Possible Toxin Contamination Removal

Food safety and quality awareness have reached significant relevance as consumers are more interested in authentic foods and drinks with specific organoleptic values. Among foodstuffs, grape products can be contaminated by Ochratoxin A (OTA), a mycotoxin that can occur in red grape after infection with Aspergillus carbonarius. The high affinity of grape pomace with OTA makes its use advantageous as an adsorbing/decontaminating material whether the pomace is fresh, has undergone pressing, or has undergone a stabilizing process. The effects of different grape repassage treatments on wine metabolic profiles were studied by 1H NMR spectroscopy coupled with metabolomics. The relative quantification of discriminating metabolites for activated-carbon-treated samples revealed higher levels of ethyl acetate and succinate than for the grape-pomace-repassed wine samples. On the contrary, the latter exhibited a relatively high content of glycerol, lactate, tartaric, isobutanol, isopentanol, and polyphenols. Although a specific decrease in aromatic compounds such as gallic acid, tyrosine, and tyrosol was also observed compared with the controls, for the pomace-based processes, the activated carbon treatment led to a marked general impoverishment of the metabolomic profiles, with a reduction in organic acids and glycerol. The repassage of wine over the grape pomace did not significantly affect the quality attributes of the wine, offering an alternative natural adsorbing/decontaminating material for the removal of OTA.

Real-time monitoring of fermentation processes in wine production with benchtop 1H NMR spectroscopy

The flexibility, affordability and ease of use of benchtop 1H NMR spectroscopy makes it potentially very interesting for assessing the quality of wine types and monitoring the fermentation process. However, the low spectral resolution of benchtop 1H NMR spectroscopy and the complexity of the mixtures hinder the direct quantification of important wine parameters and, thus, prevent its widespread use as an analytical tool in wineries. We show here that these problems can be solved using model-based data processing. In a first step, the accuracy of the new approach was evaluated by analyzing gravimetrically prepared test mixtures representing different fermentation stages. Good agreement was found, demonstrating the reliability of the new method. In a second step, benchtop 1H NMR spectroscopy combined with model-based data processing was used for the real-time monitoring of real fermentation media. Wine fermentation processes with different feed strategies (batch and fed-batch) were investigated and compared and the evolution of important wine constituents as well as effects caused by the different feeding strategies were monitored, demonstrating the applicability of the new approach also in demanding applications. This opens the way to using benchtop NMR spectroscopy for optimization and decision making in wine production.

Identifying the botanical origin of alcohol using 2H SNIF NMR: A case study of "polish vodka" PGI

In this study, we utilized 2H SNIF NMR and chemometric techniques to differentiate the botanical origin of raw materials used in vodka production in Poland, specifically focusing on plants with C3 metabolism such as grain, potato, and sugar beet. Additionally, for the first time, mixtures of alcohols from different C3 plants were analysed to detect adulteration. Our goal was to determine if it is possible to detect the addition of a different raw material in vodka made from a mixture of alcohols derived from C3 plants. Significant isotopic differences were confirmed using analysis of variance and Tukey's tests. Linear relationships in grain-potato, grain-sugar beet, and beet-potato mixtures enabled composition determination. The detectability threshold for adulterants ranged from 10 % to 50 %, depending on the type of raw material. Our findings suggest that 2H SNIF-NMR is an effective tool for authenticating vodka and detecting adulteration in products marketed as "Polish vodka."

Metabolic Profiling and Stable Isotope Analysis of Wines: Pilot Study for Cross-Border Authentication

Globalization and free market dynamics have significantly impacted state economies, particularly in the wine industry. These forces have introduced greater diversity in wine products but have also heightened the risk of food fraud, especially in high-value commodities like wine. Due to its market value and the premium placed on quality, wine is frequently subject to adulteration. This issue is often addressed through regulatory trademarks on wine labels, such as Protected Designation of Origin (PDO) and Protected Geographic Indication (PGI). In this context, the metabolic profiles (organic acids, carbohydrates, and phenols) and stable isotope signatures (δ13C, δ18O, D/HI, and D/HII) of red and white wines from four agroclimatically similar regions were examined. The study explored how factors such as grape variety, harvest year, and geographical origin affect wine composition, with a particular focus on distinguishing samples from cross-border areas. Multivariate statistical analysis was used to assess the variability in wine composition and to identify distinct groups of samples. Preliminary results revealed that organic acids and volatile compounds were found in lower concentrations than carbohydrates but were significantly higher than phenols, with levels ranging between 1617 mg/L and 6258 mg/L. Carbohydrate content in the wines varied from 8285 mg/L to 14662 mg/L. Principal Component Analysis (PCA) indicated certain separation trends based on the variance in carbohydrates (e.g., fructose, glucose, galactose) and isotopic composition. However, Discriminant Analysis (DA) provided clear distinctions based on harvest year, variety, and geographical origin.

Novel Pseudo-Two-Dimensional 19F NMR Spectroscopy for Rapid Simultaneous Detection of Amines in Complex Mixture

Rapid detection of amines in complex mixtures presents a significant challenge. Here, we introduce a novel nuclear magnetic resonance (NMR) method for amine detection utilizing a probe with two fluorine atoms in distinct chemical environments. Upon interaction with an amine, the probe generates two atomic resonance peaks, which are used to create coordinates, revealing fluorine chemical shifts on the 19F NMR spectroscopy. This innovative approach allows for the clear distinction of amine signals in a two-dimensional plane. This method has been effectively employed in analyzing amines in pharmaceuticals and amino acids in Ophiopogon japonicus and dry white wine, providing a robust and general approach for amine analysis.

Automatic Chemical Profiling of Wine by Proton Nuclear Magnetic Resonance Spectroscopy

We report the development of MagMet-W (magnetic resonance for metabolomics of wine), a software program that can automatically determine the chemical composition of wine via 1H nuclear magnetic resonance (NMR) spectroscopy. MagMet-W is an extension of MagMet developed for the automated metabolomic analysis of human serum by 1H NMR. We identified 70 compounds suitable for inclusion into MagMet-W. We then obtained 1D 1H NMR reference spectra of the pure compounds at 700 MHz and incorporated these spectra into the MagMet-W compound library. The processing of the wine NMR spectra and profiling of the 70 wine compounds were then optimized based on manual 1H NMR analysis. MagMet-W can automatically identify 70 wine compounds in most wine samples and can quantify them to 10-15% of the manually determined concentrations, and it can analyze multiple spectra simultaneously, at 10 min per spectrum. The MagMet-W Web server is available at https://www.magmet.ca.

Flower Species Ingredient Verification Using Orthogonal Molecular Methods

Flowers are gaining considerable interest among consumers as ingredients in food, beverages, cosmetics, and natural health products. The supply chain trades in multiple forms of botanicals, including fresh whole flowers, which are easier to identify than dried flowers or flowers processed as powdered or liquid extracts. There is a gap in the scientific methods available for the verification of flower species ingredients traded in the supply chains of multiple markets. The objective of this paper is to develop methods for flower species ingredient verification using two orthogonal methods. More specifically, the objectives of this study employed both (1) DNA-based molecular diagnostic methods and (2) NMR metabolite fingerprint methods in the identification of 23 common flower species ingredients. NMR data analysis reveals considerable information on the variation in metabolites present in different flower species, including color variants within species. This study provides a comprehensive comparison of two orthogonal methods for verifying flower species ingredient supply chains to ensure the highest quality products. By thoroughly analyzing the benefits and limitations of each approach, this research offers valuable insights to support quality assurance and improve consumer confidence.

Nuclear Magnetic Resonance Fingerprints and Mini DNA Markers for the Authentication of Cinnamon Species Ingredients Used in Food and Natural Health Products

Cinnamomum verum (syn C. zeylanicum) is considered 'true' cinnamon. However, it is reported that less expensive sources of cinnamon from C. cassia (syn C. aromaticum), C. loureiroi, and C. burmannii (toxic coumarin) may be used in the place of C. verum. We lack the quality assurance tools that are required to differentiate C. verum from other cinnamon species when verifying that the correct species is sourced from ingredient suppliers. The current research on cinnamon species authentication using DNA tools is limited to a few species and the use of high-quality DNA extracted from raw leaf materials. The cinnamon bark traded in the supply chain contains much less DNA and poorer-quality DNA than leaves. Our research advances DNA methods to authenticate cinnamon, as we utilized full-length chloroplast genomes via a genome skimming approach for C. burmannii and C. cassia to facilitate the design of optimal mini DNA markers. Furthermore, we developed and validated the use of NMR fingerprints for several commercial cinnamon species, including the quantification of 16 molecules. NMR fingerprints provided additional data that were useful for quality assessment in cinnamon extract powders and product consistency. Both the new mini DNA markers and NMR fingerprints were tested on commercial cinnamon products.

Modified PSYCHE NMR - The possibility for the direct semi-quantitation of components in mixtures

A series of commonly occurring biologically relevant compounds were analysed using internally referenced PSYCHE NMR and the accuracy/precision compared with that achieved using conventional qNMR. The effect of chemical shift, coupling constants, swept frequency pulse angle, excitation sculpting and the impact of signal overlap in mixtures was evaluated in terms of statistically significant variation between the two techniques. It is shown that suitably optimised PSYCHE NMR represents a potentially reliable method for the semi-quantitation of mixtures of compounds, whose spectroscopic signals overlap in conventional qNMR analysis and hence cannot be accurately quantified with that technique. This is of particular relevance for complex mixtures of natural products. Of particular note is the effect of the excitation sculpting block introduced to supress baseline artefacts.

Origin tracing and adulteration identification of bird's nest by high- and low-field NMR combined with pattern recognition

Edible bird's nest (EBN) is a high-value health food with various nutrients and bioactive components. With increasing demand for EBN, they are often adulterated with cheaper ingredients or falsely labeled by the origin information, thus harming consumer interests. In this study, high- and low-field nuclear magnetic resonance (HF/LF-NMR) technology combined with multivariate statistical analysis was used to identify the geographical marker of EBN from different origins and authenticate the adulterated EBN with various adulterants at different adulteration rates. Authentic EBN samples from Malaysia were used to simulate adulteration using gelatin (GL), agar (AG) and starch (ST) at 10 %, 20 %, 40 %, 60 %, 80 %, and 100 % w/w, respectively. The results showed significant differences in composition among EBN from different origins, with isocaproate and citric acid serving as geographical markers for Malaysia and Vietnam, respectively. Leucine, glutamic acid, and N-acetylglycoprotein serving as geographical markers for Indonesia. In addition, PLS model further verified the accuracy of origin identification of EBN. The LF-NMR results of adulteration EBN showed a linear correlation between the transverse relaxation (T2, S2) and the adulterated ratio. The OPLS-DA based on T2 spectra could accurately identify authentic EBN from adulterated with GL, AG and ST at 40 %, 20 %, and 20 %, respectively. Fisher discrimination model was able to differentiate at 20 %, 20 %, and 40 %, respectively. These results show that the 1H NMR combined with multivariate statistical analysis method could be a potential tool for the detection of origin and adulteration of EBN.

Beyond the Bottle: Exploring Health-Promoting Compounds in Wine and Wine-Related Products-Extraction, Detection, Quantification, Aroma Properties, and Terroir Effects

Health-promoting compounds in wine and wine-related products are important due to their potential benefits to human health. Through an extensive literature review, this study explores the presence of these compounds in wine and wine-related products, examining their relationship with terroir and their impact on the aromatic and flavor properties that are perceived orally: sunlight exposure, rainfall patterns, and soil composition impact grapevines' synthesis and accumulation of health-promoting compounds. Enzymes, pH, and the oral microbiome are crucial in sensory evaluation and perception of health promotion. Moreover, their analysis of health-promoting compounds in wine and wine-related products relies on considerations such as the specific target compound, selectivity, sensitivity, and the complexity of the matrix.

Characterization of Bioactivity of Selective Molecules in Fruit Wines by FTIR and NMR Spectroscopies, Fluorescence and Docking Calculations

Fourier transform infrared (FTIR) and proton nuclear magnetic resonance (1H NMR) spectroscopies were applied to characterize and compare the chemical shifts in the polyphenols' regions of some fruit wines. The obtained results showed that FTIR spectra (1800-900 cm-1) and 1H NMR (δ 6.5-9.3 ppm) of different fruit wines can be used as main indices of the year of vintage and quality of fruit wines. In addition to the classical determination of antioxidant profiles and bioactive substances in wines, fluorometric measurements were used to determine the interactions of wine substances with the main human serum proteins. The results showed relatively high binding properties of wines with the highest one for pomegranate, followed by kiwifruit and persimmon wines. The interactions of vitamin C, catechin and gallic acid with human serum albumin (HSA) were also examined by docking studies. The docking calculations showed that gallic acid has a stronger binding affinity compared to catechin and vitamin C. The stronger binding affinity of gallic acid may be due to three hydrogen bonds and pi-pi interactions. The fluorescence and docking studies proved that only the bioactive compounds of wines and not the amount of alcohol have high binding properties to human serum proteins. The emphasis in this report was made on the utility of FTIR, NMR and fluorescence of wines as a mean of wine authentication and its fingerprint. The findings, based on polyphenols from fruits and fruit wines, their bioactivity and health properties, offer valuable insights for future endeavours focused on designing healthy food products.

Chemical Profiling of Wines Produced in Bulgaria and Distinction from International Grape Varieties

Distinguishing the botanical and geographical origin of wine is important to prevent wine adulteration and to determine its quality. The combined use of ¹H NMR profiling and chemometrics allows the quantification of 31 common organic components in the NMR spectra of 70 wines from different sources. Using the NMR metabolomics approach, a successful differentiation of wines produced from Bulgarian and international grape varieties is achieved using linear discriminant analysis. Wines produced from typical local grape varieties contain higher average amounts of galacturonic, malic, tartaric, and succinic acid, alanine, choline, several alcohols, and saccharides arabinose, galactose, and sucrose than imported wine assortments. A practical decision tree is proposed for distinguishing 15 different grape varieties based on the amounts of the common wine components. An example of distinction of real from diluted wine via creation of a PLS-DA model is presented. Wines from the two subregions officially recognized by the EU at the Protected Geographical Indication (PGI) level are unequivocally recognized.

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.

Nontargeted 1 H NMR fingerprinting and multivariate statistical analysis for traceability of Greek PDO Vostizza currants

In this study, non-targeted ¹ H NMR fingerprinting was used in combination with multivariate statistical analyses for the classification of Greek currants based on their geographical origins (Aeghion, Nemea, Kalamata, Zante, and Amaliada). As classification techniques, Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were carried out. To elucidate different components according to PDO (Protected Designation of Origin), products from Aeghion (Vostizza) were statistically compared with each one of the four other regions. PLS-DA plots ensure that currants from Kalamata, Nemea, Zante, and Amaliada are well classified with respect to the PDO currants, according to differences observed in metabolites. Results suggest that composition differences in carbohydrates, amino, and organic acids of currants are sufficient to discriminate them in correlation to their geographical origin. In conclusion, currants metabolites which mostly contribute to classification performance of such discriminant analysis model present a suitable alternative technique for currants traceability. The study results contribute information to the currants' metabolite fingerprinting by NMR spectroscopy and their geographical origin. PRACTICAL APPLICATION: This study presents an analytical approach for a high nutritional value Greek PDO product, Vostizza currant. A further research and implementation of this method in food industry, can be the key to food fraud incidents. Thus, application of this work opens up posibilities to "farm to table" mission.

A Review of Wine Authentication Using Spectroscopic Approaches in Combination with Chemometrics

In a global context where trading of wines involves considerable economic value, the requirement to guarantee wine authenticity can never be underestimated. With the ever-increasing advancements in analytical platforms, research into spectroscopic methods is thriving as they offer a powerful tool for rapid wine authentication. In particular, spectroscopic techniques have been identified as a user-friendly and economical alternative to traditional analyses involving more complex instrumentation that may not readily be deployable in an industry setting. Chemometrics plays an indispensable role in the interpretation and modelling of spectral data and is frequently used in conjunction with spectroscopy for sample classification. Considering the variety of available techniques under the banner of spectroscopy, this review aims to provide an update on the most popular spectroscopic approaches and chemometric data analysis procedures that are applicable to wine authentication.

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.

A Methodology Based on FT-IR Data Combined with Random Forest Model to Generate Spectralprints for the Characterization of High-Quality Vinegars

Sherry wine vinegar is a Spanish gourmet product under Protected Designation of Origin (PDO). Before a vinegar can be labeled as Sherry vinegar, the product must meet certain requirements as established by its PDO, which, in this case, means that it has been produced following the traditional solera and criadera ageing system. The quality of the vinegar is determined by many factors such as the raw material, the acetification process or the aging system. For this reason, mainly producers, but also consumers, would benefit from the employment of effective analytical tools that allow precisely determining the origin and quality of vinegar. In the present study, a total of 48 Sherry vinegar samples manufactured from three different starting wines (Palomino Fino, Moscatel, and Pedro Ximénez wine) were analyzed by Fourier-transform infrared (FT-IR) spectroscopy. The spectroscopic data were combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) and principal component analysis (PCA), as well as other nonparametric supervised techniques, namely, support vector machine (SVM) and random forest (RF), for the characterization of the samples. The HCA and PCA results present a clear grouping trend of the vinegar samples according to their raw materials. SVM in combination with leave-one-out cross-validation (LOOCV) successfully classified 100% of the samples, according to the type of wine used for their production. The RF method allowed selecting the most important variables to develop the characteristic fingerprint (“spectralprint”) of the vinegar samples according to their starting wine. Furthermore, the RF model reached 100% accuracy for both LOOCV and out-of-bag (OOB) sets.