Classification of Chinese wine varieties using 1H NMR spectroscopy combined with multivariate statistical analysis

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.

From Nontargeted to Targeted Analysis: Feature Selection in the Differentiation of Truffle Species (Tuber spp.) Using 1H NMR Spectroscopy and Support Vector Machine

The price of different truffle types varies according to their culinary value, sometimes by more than a factor of 10. Nonprofessionals can hardly distinguish visually the species within the white or black truffles, making the possibility of food fraud very easy. Therefore, the identification of different truffle species (Tuber spp.) is an analytical task that could be solved in this study. The polar extract from a total of 80 truffle samples was analyzed by 1H NMR spectroscopy in combination with chemometric methods covering five commercially relevant species. All classification models were validated applying a repeated nested cross-validation. In direct comparison, the two very similar looking and closely related black representatives Tuber melanosporum and Tuber indicum could be classified 100% correctly. The most expensive truffle Tuber magnatum could be distinguished 100% from the other relevant white truffle Tuber borchii. In addition, signals for a potential Tuber borchii and a potential Tuber melanosporum marker for targeted approaches could be detected, and the corresponding molecules were identified as betaine and ribonate. A model covering all five truffle species Tuber aestivum, Tuber borchii, Tuber indicum, Tuber magnatum, and Tuber melanosporum was able to correctly discriminate between each of the species.

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.

Identification of Oak-Barrel and Stainless Steel Tanks with Oak Chips Aged Wines in Ningxia Based on Three-Dimensional Fluorescence Spectroscopy Combined with Chemometrics

With the increased incidence of wine fraud, a fast and reliable method for wine certification has become a necessary prerequisite for the vigorous development of the global wine industry. In this study, a classification strategy based on three-dimensional fluorescence spectroscopy combined with chemometrics was proposed for oak-barrel and stainless steel tanks with oak chips aged wines. Principal component analysis (PCA), partial least squares analysis (PLS-DA), and Fisher discriminant analysis (FDA) were used to distinguish and evaluate the data matrix of the three-dimensional fluorescence spectra of wines. The results showed that FDA was superior to PCA and PLS-DA in classifying oak-barrel and stainless steel tanks with oak chips aged wines. As a general conclusion, three-dimensional fluorescence spectroscopy can provide valuable fingerprint information for the identification of oak-barrel and stainless steel tanks with oak chips aged wines, while the study will provide some theoretical references and standards for the quality control and quality assessment of oak-barrel aged wines.

Identification of Chinese red wine origins based on Raman spectroscopy and deep learning

In this study, we combined Raman spectroscopy with deep learning for the first time to establish an accurate, simple, and fast method to identify the origin of red wines. We collected Raman spectra from 200 red wine samples of the Cabernet Sauvignon variety from four different origins with a portable Raman spectrometer. The red wine samples, made in 2021, were from the same producer in China. Differences were found by analyzing the Raman spectra of red wine samples. These differences are mainly caused by ethanol, carboxylic acids, and polyphenols. After further analysis, for different origins, the different performances of these substances on the Raman spectrum are related to the climate and geographical conditions of the origin. The Raman spectra were analyzed by principal component analysis (PCA). The data with PCA dimensionality reduction were imported into an artificial neural network (ANN), multifeature fusion convolutional neural network (MCNN), GoogLeNet, and residual neural network (ResNet) to establish red wine origin identification models. The classification results of the model prove that climate, geography, and other conditions can provide support for the classification of red wine origin. The experiments showed that all four models performed well, among which MCNN performed the best with 93.2% classification accuracy, and the area under the curve (AUC) was 0.987. This study provides a new means to classify the origin of red wine and opens up new ideas for identifying origins in the food field.

NMR-Based Approaches in the Study of Foods

In this review, the three different NMR-based approaches usually used to study foodstuffs are described, reporting specific examples. The first approach starts with the food of interest that can be investigated using different complementary NMR methodologies to obtain a comprehensive picture of food composition and structure; another approach starts with the specific problem related to a given food (frauds, safety, traceability, geographical and botanical origin, farming methods, food processing, maturation and ageing, etc.) that can be addressed by choosing the most suitable NMR methodology; finally, it is possible to start from a single NMR methodology, developing a broad range of applications to tackle common food-related challenges and different aspects related to foods.

Untargeted metabolomic analysis of Chinese red wines for geographical origin traceability by UPLC-QTOF-MS coupled with chemometrics

Identifying geographical origins of red wines made in specific regions is of significance since the false claim of geographical origins has been frequently exposed in China's wine industry. In this work, an untargeted metabolomic approach based on UPLC-QTOF-MS was established to discriminate geographical origins of Chinese red wines. The principal component analysis (PCA) showed significant differences between wine samples from three famous geographical origins in China. The metabolites contributing to the differentiation were screened by orthogonal partial least squares-discriminant analysis (OPLS-DA) with pairwise modeling. 40 and 46 differential metabolites in positive and negative ionization modes were putatively identified as chemical markers. Furthermore, heatmap visualization and OPLS-DA models were constructed based on these identified markers and external verification wine samples from different regions were successfully discriminated, with recognition rate up to 96.7%. This study indicated that UPLC-QTOF-MS-based untargeted metabolomics has great potential for the geographical origin traceability of Chinese red wines.

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.

1H NMR Combined with Multivariate Statistics for Discrimination of Female and Male Flower Buds of Populus tomentosa

¹H Nuclear Magnetic Resonance (¹H NMR) combined with multivariate statistics was adopted to discriminate female and male flower buds of Populus tomentosa in the study. Samples of 11 female and 16 male flower buds of P. tomentosa were collected in Beijing, China. ¹H NMR spectra were acquired on a 400 MHz spectrometer. In total, 30 chemical compounds were identified with standards and literature according to chemical shifts, peak areas, and multiplicity. Principal component analysis (PCA), hierarchical clustering analysis (HCA), and supervised orthogonal partial least squares-discriminant analysis (OPLS-DA) were applied to discriminate female and male flower buds. An apparent grouping trend (R²X, 0.809; Q², 0.903) between female and male groups was exhibited with PCA and HCA. The two groups were also well discriminated with OPLS-DA (R²X, 0.808; R²Y, 0.976; Q², 0.960). Combined with variable importance in projection (VIP) > 1.0 and p < 0.05 of OPLS-DA, it was found that the content of daucosterol, β-sitosterol, ursolic acid, and betulonic acid in male group was higher than that in female, which should be the key differences of chemical constituents in female and male flower buds of P. tomentosa. The study demonstrated that ¹H NMR combined with multivariate statistics could be used to discriminate female and male plants and clarify differences, which provided a novel method to identify the gender of dioecious plants.

Recent advances in NMR-based metabolomics of alcoholic beverages

Alcoholic beverages have a complex chemistry that can be influenced by their alcoholic content, origin, fermentation process, additives, and contaminants. The complex composition of these beverages leave them susceptible to fraud, potentially compromising their authenticity, quality, and market value, thus increasing risks to consumers' health. In recent years, intensive studies have been carried out on alcoholic beverages using different analytical techniques to evaluate the authenticity, variety, age, and fermentation processes that were used. Among these techniques, NMR-based metabolomics holds promise in profiling the chemistry of alcoholic beverages, especially in Asia where metabolomics studies on alcoholic beverages remain limited.

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.

Detection of fraud in lime juice using pattern recognition techniques and FT-IR spectroscopy

The lime juice is one of the products that has always fallen victim to fraud by manufacturers for reducing the cost of products. The aim of this research was to determine fraud in distributed lime juice products from different factories in Iran. In this study, 101 samples were collected from markets and also prepared manually and finally derived into 5 classes as follows: two natural classes (Citrus limetta, Citrus aurantifolia), including 17 samples, and three reconstructed classes, including 84 samples (made from Spanish concentrate, Chinese concentrate, and concentrate containing adulteration compounds). The lime juice samples were freeze-dried and analyzed using FT-IR spectroscopy. At first, principal component analysis (PCA) was applied for clustering, but the samples were not thoroughly clustered with respect to their original groups in score plots. To enhance the classification rates, different chemometric algorithms including variable importance in projection (VIP), partial least square-discriminant analysis (PLS-DA), and counter propagation artificial neural networks (CPANN) were used. The best discriminatory wavenumbers related to each class were selected using the VIP-PLS-DA algorithm. Then, the CPANN algorithm was used as a nonlinear mapping tool for classification of the samples based on their original groups. The lime juice samples were correctly designated to their original groups in CPANN maps and the overall accuracy of the model reached up to 0.96 and 0.87 for the training and validation procedures. This level of accuracy indicated the FT-IR spectroscopy coupled with VIP-PLS-DA and CPANN methods can be used successfully for detection of authenticity of lime juice samples.

What's in a wine? - A spot check of the integrity of European wine sold in China based on anthocyanin composition, stable isotope and glycerol impurity analysis

The international wine market has been repeatedly hit by cases of fraud in recent decades. While several studies attested a special vulnerability of the fast growing wine business in China, reports on chemical analyses of commercial wine samples are rare. We examined 50 predominantly red wines with European labelling, which were purchased on the Chinese market, for fraud-relevant parameters. More than 20% of the tested samples revealed anomalies in relation to the stable isotope ratios of D/H, ¹⁸O/¹⁶O and ¹³C/¹²C, contents of technical glycerol by-products or anthocyanin composition. These results strongly suggested watering of the wines, chaptalisation, glycerol addition or the use of non-Vitis anthocyanin sources, respectively. Some of these samples also showed suspicious spelling errors or other irregularities in the labelling, but the majority appeared genuine to the eye. Hence, this spot check demonstrates the importance of chemical authenticity analysis of market samples in order to detect fraudulent products. Moreover, we used the same sample set for an evaluation of the Chinese standard method for carbon stable isotope determination of wine ethanol in comparison to the current OIV (International Organisation of Vine and Wine) standard method. The results of a Bland-Altman analysis indicated that the methods can be applied interchangeably. As the two methods differ in their workflow and in the requested equipment, this might eventually enable more laboratories to perform ¹³C/¹²C analysis of wine and spirits.

Vintage analysis of Chinese Baijiu by GC and 1H NMR combined with multivariable analysis

Economical-driven counterfeit and inferior aged Chinese Baijiu has caused serious concern of publicity in China. In this study, a total of 167 authentic Chinese Baijiu samples with different vintages including 3 flavor types were carefully collected. Gas chromatography (GC) was used to determine main volatile components and proton nuclear magnetic resonance (¹H NMR) spectroscopy was employed to obtain non-targeted fingerprints of Chinese Baijiu samples. Partial least squares regression (PLSR) models, which were confirmed by internal and external validation, were established for effectively identifying actual storage vintage of Chinese Baijiu with various brands, flavor types. Centering (Ctr), pareto scaling (Par), unit variance scaling (UV) data pretreatment methods, principal components (PCs), and three modified variable selection methods were proposed to successfully optimize the vintage model and effectively extract important vintage characteristic factors. This study demonstrated that NMR and GC combined with multivariate statistical analysis are effective tools for validating vintage authenticity of Chinese Baijiu.

Real wine or not? Protecting wine with traceability and authenticity for consumers: chemical and technical basis, technique applications, challenge, and perspectives

Wine is a high-value alcoholic beverage welcomed by consumers because of its flavor and nutritional value. The key information on wine bottle label is the basis of consumers' choice, which also becomes a target for manufacturers to adulterate, including geographical origin, grape variety and vintage. With the improvement of wine adulteration technology, modern technological means are needed to solve the above mentioned problems. The chemical basis of wine determines the type of technique used. Detection technology can be subdivided into four groups: mass spectrometry techniques, spectroscopic techniques, chromatography techniques, and other techniques. Multivariate statistical analysis of the data was performed by means of chemometrics methods. This paper outlines a series of procedures for wine classification and identification, and classified the analytical techniques and data processing methods used in recent years with listing their principles, advantages and disadvantages to help wine researchers choose appropriate methods to meet the challenge and ensure wine traceability and authenticity.

Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal component analysis is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning principal component analysis is not well understood by many applied analytical scientists and spectroscopists who use principal component analysis. The meaning of features identified through principal component analysis is often unclear. This manuscript traces the journey of the spectra themselves through the operations behind principal component analysis, with each step illustrated by simulated spectra. Principal component analysis relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of principal component analysis , such as the scores representing "concentration" or "weights". The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a principal component analysis model shows how to interpret application specific chemical meaning of the principal component analysis loadings and how to analyze scores. A critical benefit of principal component analysis is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

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.

Rapid and non-destructive approach for the detection of fried mustard oil adulteration in pure mustard oil via ATR-FTIR spectroscopy-chemometrics

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy integrated with chemometrics was effectively applied for the rapid detection and accurate quantification of fried mustard oil (FMO) adulteration in pure mustard oil (PMO). PMO was adulterated with FMO in the range of 0.5-50% v/v. Principal component analysis (PCA) elucidated the studied adulteration using two components with an explained variance of 97%. The linear discriminant analysis (LDA) was adopted to classify the adulterated PMO samples with FMO. LDA model showed 100% accuracy initially, as well as when cross-validated. To enhance the overall quality of models, characteristic spectral regions were optimized, and principal component regression (PCR) and partial least square regression (PLS-R) models were constructed with high accuracy and precision. PLS-R model for the 2nd derivative of the optimized spectral region 1260-1080 cm^-1 showed best results for prediction sample sets in terms of high R² and residual predictive deviation (RPD) value of 0.999 and 31.91 with low root mean square error (RMSE) and relative prediction error (RE %) of 0.53% v/v and 3.37% respectively. Thus, the suggested method can detect up to 0.5% v/v of adulterated FMO in PMO in a short time interval.

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.

Metabolite Characterization and Correlations with Antioxidant and Wound Healing Properties of Oil Palm (Elaeis guineensis Jacq.) Leaflets via 1H-NMR-Based Metabolomics Approach

Oil palm (Elaeis guineensis Jacq.) leaflets (OPLs) are one of the major agricultural by-products generated from the massive cultivation of Malaysian palm oil. This biomass is also reported to be of potential value based on its health-improving effects. By employing proton nuclear magnetic resonance (1H-NMR) spectroscopy combined with multivariate data analysis (MVDA), the metabolite profile of OPLs was characterized and correlated with their antioxidant and wound healing properties. Principal component analysis (PCA) classified four varieties of extracts, prepared using solvents ranging from polar to medium polarity, into three distinct clusters. Cumulatively, six flavonoids, eight organic acids, four carbohydrates, and an amine were identified from the solvent extracts. The more polar extracts, such as, the ethyl acetate-methanol, absolute methanol, and methanol-water, were richer in phytochemicals. Based on partial least square (PLS) analysis, the constituents in these extracts, such as (+)-catechin, (-)-epicatechin, orientin, isoorientin, vitexin, and isovitexin, were strongly correlated with the measured antioxidant activities, comprising ferric reducing antioxidant power (FRAP), 2,2-diphenyl-1-picrylhydrazyl (DPPH), and nitric oxide (NO) free radical scavenging activities, as well as with cell proliferation and migration activities. This study has provided crucial evidence on the importance of these natural antioxidant compounds on the wound healing properties of OPL.

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

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

Investigating the Role of Antioxidant Compounds in Riboflavin-Mediated Photo-Oxidation of Methionine: A 1H-NMR Approach

Riboflavin (RF) is a well-known photosensitizer, responsible for the light-induced oxidation of methionine (Met) leading to the spoilage of wine. An NMR approach was used to investigate the role of gallic acid (GA) and sulfur dioxide (SO₂) in the RF-mediated photo-oxidation of Met. Water solutions of RF and Met, with and without GA or SO₂, were exposed to visible light for increasing time in both air and nitrogen atmospheres. Upon light exposure, a new signal appeared at 2.64 ppm that was assigned to the S(O)CH₃ moiety of methionine sulfoxide. Its formation rate was lower in a nitrogen atmosphere and even lower in the presence of GA, supporting the ability of this compound in quenching the singlet oxygen. In contrast, SO₂ caused relevant oxidation of Met, moderately observed even in the dark, making Met less available in donating electrons to RF. The competition of GA versus Met photo-oxidation was revealed, indicating effectiveness of this antioxidant against the light-dependent spoilage of wine. A pro-oxidant effect of SO₂ toward Met was found as a possible consequence of radical pathways involving oxygen.

A flavor uniformity evaluation and improvement of Chinese spirit by electronic nose

Fenjiu spirits are famous for their multifarious flavors in China. Nevertheless, the uniformity and homogeneity of the products are always challenges for the producers. Three flavor measurement methods, that is, zNose™, GC-MS, and spirit tasters, were employed and correlated for evaluation of Fenjiu flavors' difference. An automatic blending system was designed to improve the uniformity, where base liquors of Dazongjiu, Dajiu, and Daijiu, as well as pure water, were used for blending, and BPNN was employed for regression and error minimization. Results showed that zNose™ results could be correlated with GC-MS in 95%; hence, zNose™ can replace GC-MS in Fenjiu flavor measurement. With the zNose™ assistance, an experimental scale, fully automatic blending system could be optimized with different modern mathematical algorithms to improve the Fenjiu products uniformity. We believe that this detailed work will advance the scientific knowledge in the field and help to facilitate the uniformity and homogeneity of Fenjiu products.

High-Throughput 1H-Nuclear Magnetic Resonance-Based Screening for the Identification and Quantification of Heartwood Diterpenic Acids in Four Black Pine (Pinus nigra Arn.) Marginal Provenances in Greece

A high-throughput quantitative Nuclear Magnetic Resonance ¹H-NMR method was developed and applied to screen the quantity of the diterpenic resin acids in the heartwood of black pine, due to the renewed scientific interest in their medicinal properties and use in various diseases treatment. The 260 samples were taken from Pinus nigra clones, selected from four provenances of the Peloponnese (Greece), participating in a 35-year-old clonal seed orchard. Total resin acids per dry heartwood weight (dhw) varied greatly, ranging from 30.05 to 424.70 mg/g_dhw (average 219.98 mg/g_dhw). Abietic was the predominant acid (76.77 mg/g_dhw), followed by palustric acid (47.94 mg/g_dhw), neoabietic acid (39.34 mg/g_dhw), and pimaric acid (22.54 mg/g_dhw). Dehydroabietic acid was at moderate levels (11.69 mg/g_dhw), while levopimaric, isopimaric, and sandaracopimaric acids were in lower concentrations. The resin acid fraction accounted for 72.33% of the total acetone extractives. Stilbenes were presented in significant quantities (19.70%). The resin acid content was composed mainly of the abietane type resin acids (83.56%). Peloponnesian Pinus nigra heartwood was found to be the richest source of resin acids identified to date and is considered the best natural source for the production of such bioactive extracts. The results indicate a high potential for effective selection and advanced breeding of pharmaceutical and high economic value bioactive substances from Pinus nigra clones.

1H NMR metabolomics applied to Bordeaux red wines

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

Wine and grape marc spirits metabolomics

INTRODUCTION

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

Discrimination of Brazilian lager beer by 1H NMR spectroscopy combined with chemometrics

¹H NMR spectroscopy combined with chemometrics was employed to discriminate lager beer samples from two different classes, according to their style and information provided on the label. Partial replacement of barley malt by adjuncts is a common practice adopted by large breweries, which can lead to a decrease in diastatic power, requiring the use of exogenous enzymes. For this reason, small variations in the spectral profile can occur in the carbohydrates region. Many studies have focused on differentiating beers according to type and brewing process. However, there have no studies concerning the discrimination of beers of the same type that differ only in style, using ¹H NMR spectroscopy. In this study PCA (first three components explained 81.5% of the dataset variability), PLS-DA and SIMCA models proved to be powerful tool with predict power higher than 90% for distinguishing lager beers based on the raw materials employed in the brewing process.