Chinese Red Wine Research Articles

Revealing the key antioxidant compounds and potential action mechanisms of Chinese Cabernet Sauvignon red wines by integrating UHPLC-QTOF-MS-based untargeted metabolomics, network pharmacology and molecular docking approaches

In recent years, red wine drinking has become more popular in China owing to its antioxidant effects. However, the key antioxidant compounds and their action mechanisms of Chinese red wines are still unclear. Herein, the antioxidant activities and chemical compositions of 45 Chinese Cabernet Sauvignon red wine samples were determined using chemical antioxidant assays and an UHPLC-QTOF-MS-based untargeted metabolomics method. The key antioxidant compounds in red wines and potential action mechanisms were revealed by integrating network pharmacology and molecular docking approaches. Results showed that there are 8 key antioxidant compounds in the red wine samples. These compounds are involved in several metabolic pathways in the body, particularly PI3K/AKT. What's more, they bind to the core antioxidant targets through hydrogen bonding and hydrophobic interaction. Among them, myricetin, laricitrin, 2,3,8-tri-O-methylellagic acid and AKT1 have the highest binding energies. This study could provide the theoretical basis for further investigation of physiological activities and functions of Chinese red wines.

UHPLC-QTOF-MS-based untargeted metabolomic authentication of Chinese red wines according to their grape varieties

Wine is a very popular alcoholic drink owing to its health benefits of antioxidant effects. However, profits-driven frauds of wine especially false declarations of variety frequently occurred in markets. In this work, an UHPLC-QTOF-MS-based untargeted metabolomics method was developed for metabolite profiling of 119 bottles of Chinese red wines from four varieties (Cabernet Sauvignon, Merlot, Cabernet Gernischt, and Pinot Noir). The metabolites of red wines from different varieties were assessed using orthogonal partial least-squares discriminant analysis (OPLS-DA) and analyzed using KEGG metabolic pathway analysis. Results showed that the differential compounds among different varieties of red wines are mainly flavonoids, phenols, indoles and amino acids. The KEGG metabolic pathway analysis showed that indoles metabolism and flavonoids metabolism are closely related to wine varieties. Based on the differential compounds, OPLS-DA models could identify external validation wine samples with a total correct rate of 90.9 % in positive ionization mode and 100 % in negative ionization mode. This study indicated that the developed untargeted metabolomics method based on UHPLC-QTOF-MS is a potential tool to identify the varieties of Chinese red wines.

Quality properties of Chinese Sichuan-style sausages as affected by Chinese red wine, yellow rice wine and beer

ABSTRACT The objective of this research was to investigate quality properties of Chinese Sichuan-style (CSS) sausages treated with Chinese red wine (RW), yellow rice wine (YR) and Chinese beer (CB). The CSS sausages treated with 13% Chinese liquor as the control, and other three groups treated with 6.5% Chinese red wine as the RW group, 6.5% Chinese yellow rice wine as the YR group, and 6.5% Chinese beer as the CB group on the basis of 6.5% Chinese liquor addition were prepared. Results showed that RW, YR and CB group CSS sausages displayed higher L ⁎(39.46–43.44) and A w, lower pH (5.78–5.91) and protein content (33.75–37.44%) compared with those of the control. The YR group sausages had higher b ⁎ value (23.25), total area% of aldehydes (3.09%) as well as flavor score (6.72), while the CB group CSS sausages showed higher a* value (24.29), total area% of aldehydes (3.94%) and kenones (2.13%), as well as lower free thiol groups (10.88 nmol/mg protein) relative to those of the control group. No significant difference in overall quality scores were detected between YR and CB group CSS sausages. The PCA showed that RW group CSS sausages achieved similar effects as CB group for volatile, color and sensory properties, which could separate CB/YR group CSS sausages from those of the other two groups. The results of this study indicated that it was applicable to improve quality properties of CSS sausages by using 6.5% Chinese yellow rice wine or beer on the basis of adding 6.5% Chinese liquor during processing.

Geographical origin traceability and authenticity detection of Chinese red wines based on excitation-emission matrix fluorescence spectroscopy and chemometric methods

Excitation–emission matrix fluorescence (EEMF) spectroscopy combined with chemometrics was developed for the geographical origin traceability and authenticity detection of Chinese red wines. Parallel factor analysis coupled with partial least squares-discriminant analysis (PARAFAC-PLS-DA), N-way partial least squares-discriminant analysis (N-PLS-DA) and unfolded partial least squares-discriminant analysis (U-PLS-DA) were used to identify the geographical origins and adulteration of red wines. Accuracy, sensitivity, specificity and precision were used to evaluate the performances of the discriminant models. The accuracy of PARAFAC-PLS-DA for training samples and prediction samples from different origins was 0.88 and 0.83, respectively. N-PLS-DA and U-PLS-DA, which used full EEMF data as model inputs, had the same excellent performance and both provided an accuracy of 1. With regard to the identification of adulterated samples, the accuracy of PARAFAC-PLS-DA for training samples and prediction samples was 0.71 and 0.83, respectively. By comparison, in both N-PLS-DA and U-PLS-DA models, only one prediction sample with the lowest adulteration level was misclassified. N-way partial least squares regression (N-PLSR) was successfully used to quantify adulteration levels for the first time, with Rc2 > 0.98, both RMSEC and RMSEP < 6%. This study showed that EEMF combined with chemometrics is an effective method for the geographical origin traceability and authenticity detection of Chinese red wines.

Chinese red wine and cheese pairings: A preliminary study of consumer perception using check‐all‐that‐apply and hedonic tests

AbstractFood and wine pairing has been studied in marketing, sensory science, and hospitality fields to evaluate the gastronomic experience of consumers. In this study, three Chinese red wines (Cabernet Gernischt: CG, Marselan: MS, and Vitis amurensis Rupr: VR) were paired with two French cheeses (Brie and Camembert) and two Chinese cheeses [sweet cheese (SWC) and salty cheese (SAC)]. The physicochemical properties of wines and cheeses were determined. Also, sensory profiles of wine and cheese samples were collected using 100 participants by a check‐all‐that‐apply (CATA) test, and the ideal pairing of wines and cheeses was evaluated using hedonic, flavor intensity, and body‐to‐body relationship tests. The results showed that VR wine presented a sweet flavor and full body. MS and CG wines have a strong, bitter taste and a hot and rough mouthfeel than VR wine. The flavors of Camembert and Brie cheeses were richer and more complex than those of SWC and SAC. The textures of Camembert and Brie were elastic, smooth, creamy, fatty, and soft. VR wine enhanced consumer preference for all four types of cheeses (each combination liking score is more than six). Wine characteristics were suppressed and not enhanced by pairing with cheese. The ideal pair evaluation in terms of flavor intensity and body‐to‐body relationship confirmed that wines dominated these two aspects for cheese‐wine pairs.Practical ApplicationsCATA questions conducted with consumers are popular to profile and characterize food and beverage products. This investigation helps validate CATA method applications in food and wine pairing areas. In addition, the hedonic, flavor intensity, and body‐to‐body tests of Chinese wine and Chinese cheese pairing are helpful in promoting the development of the Chinese wine and cheese industry. The findings provide future research directions in pairing principles of food and beverages.

Geographical origin identification of Chinese red wines using ultraviolet-visible spectroscopy coupled with machine learning techniques

Identifying geographical origins of red wines produced in specific regions is of great importance, since the geographical origins of wine influence its quality and price greatly. In this study, the feasibility of UV-Vis spectroscopy was evaluated for the classification of Chinese red wine samples according to their geographical origins, using principal component analysis (PCA) and two machine learning techniques: orthogonal partial least squares-discriminant analysis (OPLS-DA) and support vector machine (SVM). The PCA analysis indicated that there are differences in the chemical composition between wine samples from three different origins and inferred the chemical compounds responsible for the discrimination between wine geographical origins. Furthermore, OPLS-DA and SVM models were established to predict the class membership of wine samples from three different origins and results showed that both models can provide correct recognition rates of 100 % for wine samples in training and prediction sets. This study demonstrated that the combination of UV-Vis spectroscopy with machine learning-based modeling has the potential to be a simple, fast and low-cost tool for the routine identification of geographical origins of Chinese red wines.

Untargeted metabolomics analysis based on LC-IM-QTOF-MS for discriminating geographical origin and vintage of Chinese red wine

Identifying wine geographical origin and vintage is vital due to the abundance of fraudulent activity associated with wine mislabeling of region and vintage. In this study, an untargeted metabolomic approach based on liquid chromatography/ion mobility quadrupole time-of-flight mass spectrometry (LC-IM-QTOF-MS) was used to discriminate wine geographical origin and vintage. Wines were well discriminated according to region and vintage with orthogonal partial least squares-discriminant analysis (OPLS-DA). The differential metabolites subsequently were screened by OPLS-DA with pairwise modeling. 42 and 48 compounds in positive and negative ionization modes were screened as differential metabolitesfor the discrimination of different wine regions, and 37 and 35 compounds were screened for wine vintage. Furthermore, new OPLS-DA models were performed using these compounds, and the external verification trial showed excellent practicality with an accuracy over 84.2%. This study indicated that LC-IM-QTOF-MS-based untargeted metabolomics was a feasible tool for wine geographical origin and vintage discrimination.

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.

Investigation of the Phenolic Component Bioavailability Using the In Vitro Digestion/Caco-2 Cell Model, as well as the Antioxidant Activity in Chinese Red Wine.

Red wine is a well-known alcoholic beverage, and is known to have phenolic compounds (PCs), which contribute to its antioxidant activity and have other beneficial advantages for human health. The aim of this study was to evaluate the effect of the simulated gastro-intestinal digestion and the Caco-2 transepithelial transport assay on the PCs, bioavailability, and the antioxidant capacity of red wines. The contents of PCs in red wine were significantly reduced during most of the digestion phases. Phenolic acid had the greatest permeability, while the flavonols had the weakest. The bioavailability of PCs ranged from 2.08 to 24.01%. The result of the partial least squares structural equation model showed that the three phenols were positively correlated with the antioxidant activity of red wine. The contribution of anthocyanins was the largest (0.8667).

Survey of the phenolic content and antioxidant properties of wines from five regions of China according to variety and vintage

Winemaking is an important agricultural economic activity worldwide, and variation in terroir gives birth to unique wines with different phenolic properties that reveal characteristics of each region, variety and vintage. We have comprehensively analyzed the phenolic components and antioxidant properties of 108 Chinese wines from five regions and four varieties produced from 2003 to 2019 by the LC-IM-QTOF-MS assay and multivariate statistical analysis. The results showed that the varieties of Cabernet Sauvignon, Cabernet Gernischt, and Merlot could be traced in Chinese red wines through the concentration of tannins. PCA showed that the first two principal components of the monomeric phenols explained 97.95% of the variance, and three regression variables were identified: (+)-catechin (CA), (−)-epicatechin (EC), and gallic acid (GA). CA and EC proved to be more stable than GA in different vintages. A cluster heatmap analysis showed that regional origin was significantly correlated with the phenolic content related to the antioxidant properties of the wine. The result reveals the similarities and differences in Chinese wines according to their regional origin, variety, and vintage.

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.

Development of an HPLC-DAD Method Combined with Chemometrics for Differentiating Geographical Origins of Chinese Red Wines on the Basis of Phenolic Compounds

In this work, a high-performance liquid chromatography-diode array detection (HPLC-DAD) method combined with chemometrics was developed for differentiating geographical origins of Chinese red wines. Phenolic compounds were firstly detected and quantified by direct injection HPLC-DAD method in thirty red wines originated from two famous wine-growing regions in China. Given the high complexity of the wine samples and the co-elution problems during chromatographic separation, multivariate curve resolution-alternating least squares (MCR-ALS) was utilized to extract pure chromatographic and spectral profiles of each co-eluted compound. Quantification of phenolic compounds was thus achieved even in the presence of overlapping peaks and uncalibrated interferents. The quantitative data of the phenolic compounds in wines provided by the MCR-ALS algorithm were afterwards analyzed by unsupervised principal component analysis (PCA) and hierarchical cluster analysis (HCA) methods and supervised orthogonal partial least squares-discrimination analysis (OPLS-DA) method. Both PCA and HCA results showed that the thirty red wines from different geographical origins could be obviously distinguished, and the OPLS-DA could successfully predict the geographical origins of red wines. This study demonstrated that the HPLC-DAD method combined with chemometrics can be used as an effective tool for differentiating geographical origins of Chinese red wines on the basis of phenolic compounds.

A hybrid acceleration strategy for nonparallel support vector machine

Nonparallel support vector machine is a binary classifier which has better sparsity and classification performance than other support vector machine models. However, it works slowly. Although sample selection is an effective acceleration approach, it will disturb the accuracy. Fortunately, another recently emerging screening method could speedup the solving process and keeps the accuracy unchanged. Motivated by the above research, a novel two-stage hybrid screening rule based on variational inequality and duality gap is proposed in this paper. It can reduce the scale of dual problem by deleting more redundant samples. Then, a better acceleration effect will be obtained. Meanwhile, it is still a safe method which means that the optimal solution remains unchanged. Moreover, Shrinking technique is embedded into the fast iterative algorithm DCDM to get further speedup. Thus the hybrid method can reduce the size of the problem, and accelerate the solving process greatly. Numerical experiments on ten benchmark data sets and a real Chinese red wine data set are conducted to verify the effectiveness of our hybrid acceleration strategy.

Chemometric analysis of Chinese red wines using stir bar sorptive extraction combined with GC–MS analysis

The aim of this work was to compare EG–Silicone and PDMS polymeric phases based on stir bar sorptive extraction method for the analysis of volatile compounds in Chinese red wines. The impact of vintages, regions and grape varieties on volatile compounds was also evaluated, and chemometric analyses were applied to achieve a possible differentiation of the wines. The results demonstrated that EG–Silicone Twister high correlation coefficients and low standard deviations were obtained for 58 major volatile compounds of wine by use of EG–silicon. Significant differences in the levels of certain volatiles were observed according to cultivars, vintages and geographical origins through analysis of variance. A satisfactory linear discriminant analysis resulted for red wines on the basis of cultivars, vintages and geographical origins was observed, in which the correct classification was 100% and the leave-one-out validation accuracy was 96.3%.

Quality evaluation of Chinese red wine based on cloud model.

Determining the quality of red wine is based on many qualitative and quantitative factors. Compared with other evaluation methods, the cloud model has an uncertainty transformation between a qualitative concept and its corresponding quantitative value, and the uncertainty transformation included fuzziness and randomness, which is suitable for solving the complexity of red wine evaluation. This study introduced the cloud model into quality evaluation of red wine for the first time, and a novel algorithm of comprehensive cloud model was proposed based on an addition algorithm of two cloud models. Furthermore, to validate the cloud model adopted in our red wine evaluation system, we used the gray relational analysis and fuzzy evaluation method. The evaluation result for the red wine sample was Good, and the result confirmed that our cloud model can be used to evaluate the quality of red wine. PRACTICAL APPLICATIONS: In 2013, China surpassed France to become the largest country of red wine consumption in the world. Red wine is made by a natural fermentation process. There are several components that make up red wine, but the most abundant is grape juice. Ethyl alcohol is the second most abundant element and it is made naturally by the fermentation of the sugar in grape. There are more than 1,000 remaining components in the recipe for red wine, where 300 are comparatively important. Although the proportion of these components is not high, they are important factors in determining the quality of red wine. Sensory evaluation is the most common method used to determine the quality of red wine. This work has identified a cloud model that can be used, based on sensory evaluation, to determine the quality of red wine.

Detection Method Optimization, Dynamic Changes during Alcoholic Fermentation and Content Analysis of “Brett Character” Compounds 4-Ethylphenol (4-EP) and 4-Ethylguaiacol (4-EG) in Chinese Red Wines

A simple and convenient HS-SPME-GC-based detection method with high detection limit, good recoveries, highly reproducibility, minimal matrix effects and a 30-min total run time for 4-ethylphenol (4-EP) and 4-ethylguaiacol (4-EG) in red wines has been developed. The 4-EG concentration that was generated during alcoholic fermentation exhibited a biphasic trend, with an initial increase that was followed by a decrease to approximately 40–50 μg/L after alcoholic fermentation in the winery. The content of one precursor substance (ferulic acid) strongly correlated with the 4-EG content, and the 4-EG decrease observed late in fermentation may be due to its adsorption by the yeast cell wall. 4-EP was undetectable during alcoholic fermentation in the winery. Furthermore, 4-EG and 4-EP were detected in most of the commercial wines sampled from the Chinese wine market. The regions of origin, the grape varieties, and the oak barrel aging times had strong impacts on 4-EP and 4-EG.

Draft Genome Sequence of Lactobacillus plantarum XJ25 Isolated from Chinese Red Wine.

Here, we present the draft genome sequence of Lactobacillus plantarum XJ25, isolated from Chinese red wine that had undergone spontaneous malolactic fermentation, which consists of 25 contigs and is 3,218,018 bp long.

Profiles of Phenolic Acids and Flavan‐3‐ols for Select Chinese Red Wines: A Comparison and Differentiation According to Geographic Origin and Grape Variety

The profiles of phenolic acids and flavan-3-ols for the selected Chinese red wines and the potential of using phenolic acids and flavan-3-ols to differentiate the geographic origin and grape variety of wines from China are investigated in this study. Significant differences and markers could be found according to the geographical origin and grape variety. Through a canonical discriminant analysis a good differentiation was developed according to the geographic origin or grape variety, and the accuracy of the discriminant model was 88.9% and 100%, respectively. According to the phenolic acid and flavan-3-ols profiles of the wine samples and good differentiation in the region and the variety discriminant analysis, minimal fraudulent claims were noted for the Chinese red wines investigated. This study provides some help for the protection of geographical origin and monovarietal wine claims.

Feature extraction and selection from volatile compounds for analytical classification of Chinese red wines from different varieties

This work was undertaken to evaluate whether it is possible to determine the variety of a Chinese wine on the basis of its volatile compounds, and to investigate if discrimination models could be developed with the experimental wines that could be used for the commercial ones. A headspace solid-phase microextraction gas chromatographic (HS-SPME-GC) procedure was used to determine the volatile compounds and a blind analysis based on Ac/Ais (peak area of volatile compound/peak area of internal standard) was carried out for statistical purposes. One way analysis of variance (ANOVA), principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were used to process data and to develop discriminant models. Only 11 peaks enabled to differentiate and classify the experimental wines. SLDA allowed 100% recognition ability for three grape varieties, 100% prediction ability for Cabernet Sauvignon and Cabernet Gernischt wines, but only 92.31% for Merlot wines. A more valid and robust way was to use the PCA scores to do the discriminant analysis. When we performed SLDA this way, 100% recognition ability and 100% prediction ability were obtained. At last, 11 peaks which selected by SLDA from raw analysis set had been identified. When we demonstrated the models using commercial wines, the models showed 100% recognition ability for the wines collected directly from winery and without ageing, but only 65% for the others. Therefore, the varietal factor was currently discredited as a differentiating parameter for commercial wines in China. Nevertheless, this method could be applied as a screening tool and as a complement to other methods for grape base liquors which do not need ageing and blending procedures.

A survey of biogenic amines in chinese red wines

Eight biogenic amines were analyzed in 38 samples of red wine from five wine-making regions of China. The analysis was carried out by reverse phase high performance liquid chromatography with fluorescence detector and dansyl chloride precolumn derivation. Tryptamine was not found in any of the samples. Putrescine was detected in all samples (100%), followed by phenylethylamine (84.2%), spermidine (60.5%), histamine (57.8%), tyramine (57.8%), cadaverine (47.4%), and spermine (36.8%). In all the samples, the levels of aromatic and heterocyclic amines with toxicological effects were: 0–4.58 mg/l for phenylethylamine, 0–10.51 mg/l for histamine, and 0–9.13 mg/l for tyramine. The amount of histamine and tyramine in most of the samples (94.7%) was less than 8 mg/l. The amines associated with sanitary conditions were also found to be present in a very low range, between 0 and 12.98 mg/l for putrescine, 0 and 19.01 mg/l for cadaverine, respectively. In the case of other amines such as spermine and spermidine, they yielded very low levels varying between 0 and 2.64 mg/l for spermine, 0 and 3.82 mg/l for spermidine, respectively.