Home Physical Sciences Quality evaluation of Cabernet Sauvignon wines in different vintages by 1H nuclear magnetic resonance-based metabolomics
Article Open Access

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

  • Shaochen Xu , Jiangyu Zhu , Qi Zhao , Jin Gao , Huining Zhang EMAIL logo and Boran Hu EMAIL logo
Published/Copyright: March 17, 2021

Abstract

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

1 Introduction

Wine obtains several metabolites from grape berries during fermentation. Many factors, including the soil, climate, viticultural practices (soil tillage and covering), winemaking process, and vintage, contribute to the metabolite composition and content of wines [1,2,3,4,5]. So far, most studies about wines have focused on the characterization and evaluation of the biological activities of selected extractable components, whereas there is a comparative lack of research on the metabolites in wines. The common parameters used to evaluate the quality of wine are the total soluble solids, alcohol concentration, total acids, and total phenols. These basic parameters are significant, and the classical analytical methods can easily detect many other important compounds [1,6,7,8,9]. However, these parameters reflect only the health of the wine and cannot fully explain the quality of the wine. Therefore, for wine quality assessment, powerful advanced analysis methods are necessary to determine the metabolites in wines [6,10].

In proton nuclear magnetic resonance-based (1H NMR-based) metabolomics, one pair of potential information extraction and classification of samples provide a new method for evaluating metabolic functions. 1H NMR spectroscopy has been combined with principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal PLS-DA (OPLS-DA) to distinguish different wines obtained from the same variety grown in different geographical regions [1,6,11] and different varieties grown in the same geographical region [3]. Its discriminant analysis counterpart (OPLS-DA) was demonstrated as a powerful tool for the analysis of qualitative data structures while the prediction results are equivalent to classification using standard PLS-DA [12].

In this study, PCA, PLS-DA, and OPLS-DA models were employed to distinguish the Cabernet Sauvignon wines from different vintages.

2 Methodology

2.1 Cabernet Sauvignon wines from different vintages

In this study, no specific permissions were required for the research activities, and the field studies did not involve protected species.

All the wine samples were vinified in Ningxia province of Northwest China. The grapes were grown nongrafted in a single vineyard with a uniform soil type (containing gravel mainly consisting of light sierozem and an organic matter content of 0.4–1.0%) in the Helan Mountains of Ningxia Province. This area is located in the warm temperate region of the northern hemisphere with a dry continental climate characterized by dry summers and severe winters. During the growth vintage (March–October) over the last 40 years, the average temperature was 15.24°C, the rainfall was 264.45 mm, and the evaporation was 1312.0 mm. Small changes in climate were registered from 2009 to 2012, and the climate information is shown in Table 1. The vineyard was planted in 1994 in north-south lines; the line spacing is 2.5 m and the spacing within the line is 1.2–1.5 m, using standardized management.

Table 1

Climate information of the vineyard during the growth vintage (March–October) in 2009–2012

Vintages Average temperature (°C) Rainfall (mm) Evaporation (mm)
2009 17.65 243.5 1562.1
2010 17.11 233.6 1398.7
2011 15.24 262.2 1423.6
2012 16.52 251.4 1266.7

2.2 Sample origin

Samples were obtained from 2009, 2010, 2011, and 2012 vintages of Cabernet Sauvignon wines that were vinified by the GUANG XIA (YINCHUAN) HELAN MOUTAIN WINERY CO. LTD, which were named S1, S2, S3, and S4, respectively; six samples were tested for each year, and each sample had three parallel samples. The wines were vinified using the same fermentation technique and the same yeast (Lalvin CY 3079) without other chemical adjustments except for potassium metabisulfite (50 mg/L). The wines were not aged in oak barrels. After fermentation, the wines were stored in fermenting tanks (50 t).

We obtained three parallel samples of each wine from the sampling mouth. Every replicate sample was funneled into a brown glass bottle (750 mL) that was then sealed with a cork and transported to the laboratory storage (−4°C). The grapes of each vintage were harvested at similar concentrations of reducing sugar and titrable acidity (Table 2). The chemical and physical features of the wines met the China national test standard (GB/T 15038-2006), as shown in Table 3. In addition, there is a nice liner relationship (R 2 = 0.958) between the residue sugar content and the alcohol content illustrated in Figure 1, reflecting the process in which natural glucose is consumed and alcohol is produced during the fermentation.

Table 2

Grape composition at harvest

Harvest date Cultivar Reducing sugar (g/L) Titrable acidity (g/L) pH
August 15, 2009 Cabernet Sauvignon 232.7 ± 1.1 7.67 ± 0.21 3.73 ± 0.01
August 16, 2010 228.8 ± 1.5 6.98 ± 0.30 3.88 ± 0.01
August 22, 2011 220.2 ± 0.9 8.32 ± 0.24 3.23 ± 0.02
August 18, 2012 225.6 ± 1.3 7.72 ± 0.20 3.54 ± 0.01
Table 3

Physical and chemical features of the wines

Index* Vintages
2009 2010 2011 2012
Alcohol content (% Vol) 13.2 ± 0.0 12.9 ± 0.1 12.4 ± 0.1 12.8 ± 0.0
Residual sugar (g/L) 2.20 ± 0.09 2.55 ± 0.17 3.10 ± 0.12 2.50 ± 0.07
Total acid (g/L) 6.4 ± 0.0 6.7 ± 0.0 6.1 ± 0.0 5.8 ± 0.0
Volatile acid (g/L) 0.42 ± 0.02 0.45 ± 0.00 0.46 ± 0.01 0.43 ± 0.01
Dry extract (g/L) 27.9 ± 0.1 28.9 ± 0.5 27.6 ± 0.1 29.1 ± 0.3
pH 3.47 ± 0.02 3.22 ± 0.01 3.56 ± 0.03 3.73 ± 0.00
Total SO2 (mg/L) 86 ± 1 88 ± 2 82 ± 1 85 ± 0
Free SO2 (mg/L) 31 ± 0 28 ± 1 32 ± 1 33 ± 0
Methanol (mg/L) 205 ± 3 220 ± 1 214 ± 4 206 ± 2
Fe3+ (mg/L) 2.2 ± 0.1 1.9 ± 0.0 2.1 ± 0.0 2.0 ± 0.0
Cu2+ (mg/L) 0.055 ± 0.003 0.053 ± 0.001 0.065 ± 0.005 0.059 ± 0.002
K+ (mg/L) 946 ± 7 936 ± 8 957 ± 4 955 ± 11
Ca2+ (mg/L) 103 ± 3 97 ± 1 99 ± 1 102 ± 2
Tartaric acid (g/L) 2.64 ± 0.13 2.28 ± 0.05 2.32 ± 0.08 2.44 ± 0.11
Citric acid (g/L) 0.31 ± 0.00 0.28 ± 0.02 0.29 ± 0.01 0.26 ± 0.03
Lactic acid (g/L) 2.66 ± 0.04 2.64 ± 0.02 2.73 ± 0.05 2.53 ± 0.01
Color tone 12.5 ± 0.1 12.8 ± 0.1 12.3 ± 0.1 12.7 ± 0.2
Color tint 0.83 ± 0.00 0.82 ± 0.01 0.80 ± 0.00 0.81 ± 0.00
  1. *

    The methods used to determine the physical and chemical features met the China National Test Standard GB/T15038–2006.

Figure 1 
                  Linear relationship between residual sugar content (g/L) and alcohol content (% vol).
Figure 1

Linear relationship between residual sugar content (g/L) and alcohol content (% vol).

2.3 NMR sample preparation

Ten milliliters of wine were centrifuged at 4,000 rpm for 20 min, and 3 mL supernatants were frozen at −70°C for 12 h and then lyophilized for 48 h. The lyophilized wine was dissolved in 400 μL of oxalate buffer (pH = 4.0), mixed with 140 μL of D2O and 60 μL of a 0.75% 4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS) in D2O solution, and then centrifuged at 13,000 rpm for 20 min. Next 500 μL supernatants were placed in 5 mm NMR tubes. The chemical shift of DSS provided reference (δ = 0) and internal standard quantitative analyses.

All the chemical reagents were of analytical grade. D2O (99.9%) and DSS were purchased from SIGMA-ALDRICH.

2.4 1H NMR spectroscopy

1H NMR spectra were recorded on a Bruker AVANCE 600 spectrometer operating at a 1H frequency of 600.13 MHz and a temperature of 298 K using a 1H {13C/15N} probe. A NOESYPRESAT pulse sequence was used to suppress the residual water signal. A total of 256 transients were collected into 32,000 complex data points with a spectral width of 7183.9 Hz, an acquisition time of 2.3 s, a mixing time of 100 ms, and a relaxation delay of 2 s. The NMR spectra were processed with a line-broadening factor of 0.3 Hz prior to Fourier transformation.

2.5 NMR data reduction

The NMR spectral data were reduced into 0.005 ppm spectral buckets. The regions corresponding to water (4.6–4.8 ppm), incompletely removed DSS (−0.5–0.5 ppm, 1.74–1.84 ppm, and 2.90–2.95 ppm), and ethanol (1.18–1.22 ppm and 3.57–3.72 ppm) were removed by AMIX software. The data set was then imported into SIMCA-P 12.0 for multivariate statistical analysis.

2.6 Pattern recognition

We used PCA and PLS-DA to check the intrinsic variability of the data set and to separate out the different vintages of the wine, respectively. PCA was employed to examine the intrinsic variation in the data set. Applying an orthogonal signal correction (OSC) followed by PLS-DA analysis, OPLS-DA can eliminate the information that did not contribute to the discrimination. PLS-DA score plots from the 1H NMR spectra of different vintage wines were generated in pairwise comparisons, then analyzed with OPLS-DA [13,14,15]. OPLS-DA is an improvement in the PLS-DA method to discriminate two groups using multivariate data [16], combine OSC and PLS-DA analysis and filter data. The advantage of OPLS-DA compared to PLS-DA is that OPLS-DA uses a single component as the predictor of groups, while the other components describe the variation in orthogonal to the first predictive component [17].

  1. Ethical approval: The conducted research is not related to either human or animal use.

3 Results

3.1 Metabolite differences in wines of different vintages

The PCA score plot shows a clear differentiation among the Cabernet Sauvignon wines of different vintages. The models show good adaptability and high predictability with high statistical values of R 2 X (0.867) and Q 2 (0.789) (Figure 2).

Figure 2 
                  PCA score plot of Cabernet Sauvignon wines of different vintages.
Figure 2

PCA score plot of Cabernet Sauvignon wines of different vintages.

The PLS-DA and OPLS-DA models were used to compare the different vintages of wine. As shown in Figure 3, the PLS-DA score plots derived from the 1H NMR spectra of the 2009 and 2010 vintages of Cabernet Sauvignon wine had the highest values for the pairwise comparison of R 2 X and Q 2, and these indicate a clear separation between the 2009 and 2010 vintages of wine. As shown in Figure 4, the OPLS-DA score plots derived from the 1H NMR spectra of the 2009 and 2010 vintages of Cabernet Sauvignon wine had the highest values for the pairwise comparison of R 2 X and R 2 Y. Figure 4 shows a clearer separation between the 2009 and 2010 vintages of wine than Figure 3.

Figure 3 
                  PLS-DA score plot chart from 1H NMR spectra of 2009 and 2010 vintage Cabernet Sauvignon wines.
Figure 3

PLS-DA score plot chart from 1H NMR spectra of 2009 and 2010 vintage Cabernet Sauvignon wines.

Figure 4 
                  OPLS-DA score plot chart from 1H NMR spectra of 2009 and 2010 vintage Cabernet Sauvignon wines.
Figure 4

OPLS-DA score plot chart from 1H NMR spectra of 2009 and 2010 vintage Cabernet Sauvignon wines.

The complementary load plot gives the contribution of the metabolite differentiation (Figure 5). The loading plot shows that the metabolites of the 2009 vintages are higher than those of the 2010 vintages. The loading plot shows high levels of valine, glycerol, 2,3-butanediol, α-glucose, acetic acid, proline, succinic acid, sucrose, tartaric acid, gallic acid, and tyrosine in the 2009 vintages, while ethyl acetate, lactic acid, choline, β-glucose, and α-d-glucuronic acid were at relatively low levels in the 2010 vintages.

Figure 5 
                  PLS-DA loading plot chart from 1H NMR spectra of 2009 and 2010 vintage Cabernet Sauvignon wines.
Figure 5

PLS-DA loading plot chart from 1H NMR spectra of 2009 and 2010 vintage Cabernet Sauvignon wines.

Both the PLS-DA and OPLS-DA score plots showed a clear discrimination between the 2009 and 2011 vintages of the Cabernet Sauvignon wine (Figures 6 and 7), and the loading plot provides the metabolites that contributed to this discrimination (Figure 8). Higher levels of 2,3-butanediol, ethyl acetate, proline, succinic acid, glycerol, α-glucose, tartaric acid, choline, and sucrose and lower levels of lactate and α-d-glucuronic acid were detected in the 2009 vintages compared to the 2011 vintages.

Figure 6 
                  PLS-DA score plot chart from 1H NMR spectra of 2009 and 2011 vintage Cabernet Sauvignon wines.
Figure 6

PLS-DA score plot chart from 1H NMR spectra of 2009 and 2011 vintage Cabernet Sauvignon wines.

Figure 7 
                  OPLS-DA score plot chart from 1H NMR spectra of 2009 and 2011 vintage Cabernet Sauvignon wines.
Figure 7

OPLS-DA score plot chart from 1H NMR spectra of 2009 and 2011 vintage Cabernet Sauvignon wines.

Figure 8 
                  PLS-DA loading plot chart from 1H NMR spectra of 2009 and 2011 vintage Cabernet Sauvignon wines.
Figure 8

PLS-DA loading plot chart from 1H NMR spectra of 2009 and 2011 vintage Cabernet Sauvignon wines.

The PCA and OPLS-DA score plots of the 2009 and 2012 vintage Cabernet Sauvignon wines also showed clear separation (Figures 9 and 10) identified by higher levels of valine, 2,3-butanediol, proline, succinic acid, d-sucrose, tartaric acid, gallic acid, α-glucose, and β-glucose and lower levels of lactate, ethyl acetate, acetic acid, glycerol, α-d-glucuronic acid, and choline in the 2009 vintages (Figure 11).

Figure 9 
                  PLS-DA score plot chart from 1H NMR spectra of 2009 and 2012 vintage Cabernet Sauvignon wines.
Figure 9

PLS-DA score plot chart from 1H NMR spectra of 2009 and 2012 vintage Cabernet Sauvignon wines.

Figure 10 
                  OPLS-DA score plot chart from 1H NMR spectra of 2009 and 2012 vintage Cabernet Sauvignon wines.
Figure 10

OPLS-DA score plot chart from 1H NMR spectra of 2009 and 2012 vintage Cabernet Sauvignon wines.

Figure 11 
                  PLS-DA loading plot chart from 1H NMR spectra of 2009 and 2012 vintage Cabernet Sauvignon wines.
Figure 11

PLS-DA loading plot chart from 1H NMR spectra of 2009 and 2012 vintage Cabernet Sauvignon wines.

The PCA and OPLS-DA score plots of the 2010 and 2011 vintage Cabernet Sauvignon wines also showed significant separation (Figures 12 and 13). The loading plot illustrates higher levels of choline, proline, and 2,3-butanediol and lower levels of valine, lactic acid, succinic acid, and glycerol in the 2010 vintages compared to those in the 2011 vintages (Figure 14).

Figure 12 
                  PLS-DA score plot chart from 1H NMR spectra of 2010 and 2011 vintage Cabernet Sauvignon wines.
Figure 12

PLS-DA score plot chart from 1H NMR spectra of 2010 and 2011 vintage Cabernet Sauvignon wines.

Figure 13 
                  OPLS-DA score plot chart from 1H NMR spectra of 2010 and 2011 vintage Cabernet Sauvignon wines.
Figure 13

OPLS-DA score plot chart from 1H NMR spectra of 2010 and 2011 vintage Cabernet Sauvignon wines.

Figure 14 
                  PLS-DA loading plot chart from 1H NMR spectra of 2010 and 2011 vintage Cabernet Sauvignon wines.
Figure 14

PLS-DA loading plot chart from 1H NMR spectra of 2010 and 2011 vintage Cabernet Sauvignon wines.

The PCA and OPLS-DA score plots also showed significant differentiation between the Cabernet Sauvignon wines vinified in 2010 and 2012 (Figures 15 and 16). Relatively higher levels of valine and 2,3-butanediol and lower levels of lactic acid, proline, acetic acid, succinic acid, choline, glycerol, and ethyl acetate were found in the Cabernet Sauvignon wines vinified in 2010 compared to those vinified in 2012, as shown in the PLS-DA loading plot (Figure 17).

Figure 15 
                  PLS-DA score plot chart from 1H NMR spectra of 2010 and 2012 vintage Cabernet Sauvignon wines.
Figure 15

PLS-DA score plot chart from 1H NMR spectra of 2010 and 2012 vintage Cabernet Sauvignon wines.

Figure 16 
                  OPLS-DA score plot chart from 1H NMR spectra of 2010 and 2012 vintage Cabernet Sauvignon wines.
Figure 16

OPLS-DA score plot chart from 1H NMR spectra of 2010 and 2012 vintage Cabernet Sauvignon wines.

Figure 17 
                  PLS-DA loading plot chart from 1H NMR spectra of 2010 and 2012 vintage Cabernet Sauvignon wines.
Figure 17

PLS-DA loading plot chart from 1H NMR spectra of 2010 and 2012 vintage Cabernet Sauvignon wines.

The PLS-DA and OPLS-DA score plots showed clear separation between the 2011 and 2012 vintage Cabernet Sauvignon wines based on the first component (Figures 18 and 19). The corresponding loading plot showed relatively high load levels of valine, lactic acid, and succinic acid and low levels of 2,3-butanediol, proline, acetic acid, choline, glycerol, d-sucrose, acetate, α-glucose, gallic acid, and tyrosine in the 2011 vintages compared with the 2012 vintages (Figure 20).

Figure 18 
                  PLS-DA score plot chart from 1H NMR spectra of 2011 and 2012 vintage Cabernet Sauvignon wines.
Figure 18

PLS-DA score plot chart from 1H NMR spectra of 2011 and 2012 vintage Cabernet Sauvignon wines.

Figure 19 
                  OPLS-DA score plot chart from 1H NMR spectra of 2011 and 2012 vintage Cabernet Sauvignon wines.
Figure 19

OPLS-DA score plot chart from 1H NMR spectra of 2011 and 2012 vintage Cabernet Sauvignon wines.

Figure 20 
                  PLS-DA loading plot chart from 1H NMR spectra of 2011 and 2012 vintage Cabernet Sauvignon wines.
Figure 20

PLS-DA loading plot chart from 1H NMR spectra of 2011 and 2012 vintage Cabernet Sauvignon wines.

3.2 Quantitative analysis

The PLS-DA analysis and OPLS-DA analysis revealed small differences in the metabolite compositions and large differences in the metabolite concentrations in the Cabernet Sauvignon wines vinified from 2009 to 2012. The abbreviations S1–S4 represent the 2009–2012 vintages, respectively. The concentration of valine in the four vintages in order from high to low was S1 > S3 > S2 > S4, the concentration of 2,3-butanediol in order from high to low was S1 > S2 > S4 > S3, the concentration of glycerol in order from high to low was S4 > S1 > S3 > S2, the concentration of ethyl acetate in order from high to low was S4 > S2 > S1 > S3, the concentration of succinic acid in order from high to low was S4 > S3 > S1 > S2, the concentration of lactate in order from high to low was S3 > S4 > S1 > S2, the concentration of choline in order from high to low was S4 > S2 > S1 > S3, and the concentration of gallic acid in order from high to low was S1 > S4 > S3 > S2.

From the 1H NMR spectra, eight metabolites were selected, and their concentrations were calculated according to their peak areas. The results of this quantitative analysis (Figure 21) agree with that of PLS-DA and OPLS-DA.

Figure 21 
                  Comparison of the main metabolite concentrations in the 2009–2012 vintage wines. *The error bars indicate the standard deviations. S1, S2, S3, and S4 represent 2009, 2010, 2011, and 2012 vintages of Cabernet Sauvignon wines, respectively.  (a): Valine content; (b): 2,3-butanediol; (c): ethyl acetate; (d): proline; (e): succinic acid; (f): choline; (g): glycerol; (h): gallic acid.
Figure 21

Comparison of the main metabolite concentrations in the 2009–2012 vintage wines. *The error bars indicate the standard deviations. S1, S2, S3, and S4 represent 2009, 2010, 2011, and 2012 vintages of Cabernet Sauvignon wines, respectively. (a): Valine content; (b): 2,3-butanediol; (c): ethyl acetate; (d): proline; (e): succinic acid; (f): choline; (g): glycerol; (h): gallic acid.

4 Discussion

4.1 Polyols and ethyl acetate

In the present study, ethanol composes a large proportion of the wines, and the difference in the ethanol content of each wine is quite small. The ethanol signals of the samples were so intense in the spectra that they cover the signals of the other less abundant components. Therefore, ethanol was not a major discriminating compound.

The 2,3-butanediol is a by-product of fermentation in wine, probably from the reduction of acetoin or pyruvic acid [1,18]. Because the taste threshold of 2,3-butanediol is 150 mg/L, it does not usually affect the flavor. However, the average content of 2,3-butanediol in each wine was approximately 0.243 g/L, which will make the wine slightly bitter with a sticky texture. In our study, the 2011 vintage wines contained the highest levels of 2,3-butanediol.

Glycerol is formed as a by-product of alcohol fermentation. The pH, sulfite concentration, grape variety, fermentation temperature, yeast, and nitrogen composition of the wine influence the level of glycerol [1,19,20]. In our study, the winemaking conditions, such as the sulfite concentration, fermentation temperature, and yeast were approximately the same. Therefore, the glycerol contents may have resulted from the sugar contents in the grape berries.

Ethyl acetate in wine is the major ester produced by yeast, which can result in the sensory perception of volatile acidity, and has a certain odor of nail polish remover. When the content of ethyl acetate in wine is low, it can contribute fruity aroma properties, thereby increasing the complexity of the aroma and taste of the wine. Generally, the content of ethyl acetate is affected by yeast strains, fermentation temperature, amino acid content, and sulfur dioxide content in juice [21]. In this study, the 2012 Cabernet Sauvignon wine had the highest ethyl acetate content. Since the fermentation conditions and production process in different vintages were the same, the difference in ethyl acetate content may be due to the amino acid content in the juice.

4.2 Organic acids

Tartaric acid, malic acid, and citric acid in wine mostly derive from the grape berries. The concentration of tartaric acid in grape berries usually remains stable despite increases in berry volume during maturation. Precipitation is related to the brewing conditions, including fermentation temperature, pH, and concentrations of potassium and calcium [1,22]. Therefore, tartaric acid in wines cannot be used as a biomarker for describing the characteristics of wines.

Higher lactate contents in wines show that malolactic fermentation has occurred, in which bacteria completely transform into lactic acid, citric acid, and malic acid [23,24]. Therefore, we cannot detect malic acid or citric acid in dry red wine.

Succinic acid is the main nonvolatile organic acid present during alcoholic fermentation and MLF [1]. As one of the major metabolic products, succinic acid is very stable and does not change with age.

4.3 Amino acids

The wine amino acids have different origins. Some are released from dead yeast or at the end of fermentation, whereas some are indigenous to the grape and can be partially or fully metabolized by yeast; others are vinified by protein enzymatic degradation [25]. Classically, alanine is used in the growth of yeast in wine, so little is detected in the finished wine product. Proline is not a nutrient used by yeast and can therefore be used as a biological marker of wine. Lee et al. [26] states that the proline content in wine depends on the environmental factors and grape varieties. Among the four different vintages of Cabernet Sauvignon wine tested, the 2009 vintage had the highest proline content, and the 2011 vintage had the lowest level of proline. This pattern may have resulted from the greater sunshine and lower rainfall in 2009.

Valine, another amino acid biomarker, was also revealed by the PLS-DA and OPLS-DA analyses. Valine is used by yeast during fermentation and appears with yeast autolysis.

4.4 Choline

Choline is a precursor of glycine betaine, and betaine is related to homocysteine. The average level of choline in wines is 5.6 mg/100 g [27,28]. In our study, the 2012 vintage Cabernet Sauvignon wines had the highest levels of choline whereas the 2011 vintage had the lowest levels.

4.5 Carbohydrates

Glucose and fructose are the main sugars in grapes. When grape maturity begins, the glucose content in the grape is higher than the fructose content; both contents become nearly equal by harvest time. Dry wine refers to wine with a sugar level of less than or equal to 4.0 g/L. We detected sucrose, α-glucose, and β-glucose, and the differences in the concentrations of these three sugars were small. Therefore, we cannot use the carbohydrate in these wines as characteristic metabolites.

4.6 Cause of differences in the metabolites of Cabernet Sauvignon wines from different vintages

1H NMR-based metabolomics were used to study the metabolite differences in various vintages of Cabernet Sauvignon wines. Pattern recognition showed clear differentiation between the wines vinified in 2009, 2010, 2011, and 2012. The metabolites used for the differentiation were 2,3-butanediol, ethyl acetate, valine, proline, succinic acid, lactate, acetic acid, glycerol, gallic acid, and choline. Wines were vinified using the same fermentation technique, yeast, and grape varieties. Therefore, climatic factors such as average temperature, rainfall, and evaporation were the main reasons for the differences in the wine metabolites of different vintages. Most likely, the higher the average temperature, higher the evaporation; and lower rainfall in 2009 increased the sugar content of the grapes and allowed the grapes to reach optimum ripeness. Therefore, the 2009 vintage wines have the highest levels of valine, 2,3-butanediol, gallic acid, and proline. Due to the lower average temperature, higher rainfall, and higher evaporation in 2011 and 2012, these grapes experienced a long, slow ripening season. The 2011 vintage wines contained the highest level of lactic acid, and the highest levels of ethyl acetate, succinic acid, glycerol, and choline were detected in the 2012 vintage wines. Some metabolites were selected from the 1H NMR spectra and quantified according to their peak areas. The results of the quantitative analysis agreed with the PLS-DA results.

5 Conclusion

This study shows that the NMR-based metabolomics approach can effectively classify wine. Certification of a vintage’s geographical indications as well as adulteration and quality monitoring, provides the theoretical basis and technical support for this method.

Abbreviations

NMR

nuclear magnetic resonance

PCA

principal component analysis

PLS-DA

partial least squares discriminant analysis

OPLS-DA

orthogonal partial least squares discriminant analysis

DSS

4,4-dimethyl-4-silapentane-1-sulfonic acid

Acknowledgments

We gratefully acknowledge the support of the Great Wall Wines, COFCO Group, P. R. China, for providing the wine samples. The authors would like to thank Dr. William James Hardie, Dr. Guangnian Lu, Dr. Hua Li, and Mr. Yaqing Yue for their kind help in this work. We also would like to direct our deepest gratitude to the comments and suggestions by anonymous journal reviewers.

  1. Research Funding: We acknowledge financial support by the National Natural Science Foundation of China (Project No. 31271857).

  2. Author contributions: S. X. was in charge of data curation, formal analysis, software, validation, visualization, writing, reviewing, and editing; J. Z. contributed to data curation, formal analysis, and software; Q. Z. was involved in formal analysis and software; J. G. contributed to formal analysis and software; H. Z. was in charge of resources; B. H. was involved in funding acquisition, investigation, methodology, project administration, data curation, supervision, validation, and writing of the original draft.

  3. Conflict of interest: We confirm that none of the authors have any competing interests in the manuscript.

  4. Data availability statement: The data sets generated during and/or analyzed during the current study are available with the corresponding author on reasonable request.

References

[1] Son HS, Hwang GS, Ahn HJ, Park WM, Lee CH, Hong YS. Characterization of wines from grape varieties through multivariate statistical analysis of 1H NMR spectroscopic data. Food Res Int. 2009;42:1483–91.10.1016/j.foodres.2009.08.006Search in Google Scholar

[2] De Pascali S, Coletta A, Del Coco L, Basile T, Gambacorta G, Fanizzi FP. Viticultural practice and winemaking effects on metabolic profile of Negroamaro. Food Chem. 2009;161:112–9.10.1016/j.foodchem.2014.03.128Search in Google Scholar

[3] Rochfort S, Ezernieks V, Bastian SEP, Downey MO. Sensory attributes of wine influenced by variety and berry shading discriminated by NMR metabolomics. Food Chem. 2010;121:1296–304.10.1016/j.foodchem.2010.01.067Search in Google Scholar

[4] Forveffle L, Vercauteren J, Rutledge DN. Multivariate statistical analysis of two-dimensional NMR data to differentiate grapevine cultivars and clones. Food Chem. 1996;57:441–50.10.1016/0308-8146(95)00220-0Search in Google Scholar

[5] Hu B, Yue Y, Zhu Y, Wen W, Zhang F, Hardie JW. Proton nuclear magnetic resonance-Spectroscopic discrimination of wines reflects genetic homology of several different grape (V. vinifera L.) cultivars. PLoS One. 2010;10:e0142840.10.1371/journal.pone.0142840Search in Google Scholar PubMed PubMed Central

[6] Son HS, Kim KM, van den Berg F, Hwang GS, Park WM, Lee CH, et al. 1H nuclear magnetic resonance-based metabolomic characterization of wines by grape varieties and production areas. J Agric Food Chem. 2008;56:8007–16.10.1021/jf801424uSearch in Google Scholar PubMed

[7] Pereira GE, Gaudillere JP, Van Leeuwen C, Hilbert G, Lavialle O, Maucourt M, et al. and chemometrics to characterize mature grape berries in four wine-growing areas in Bordeaux, France. J Agric Food Chem. 2005;53:6382–9.10.1021/jf058058qSearch in Google Scholar PubMed

[8] Amaral FM, Caro MSB. Investigation of different pre-concentration methods for NMR analyses of Brazilian white wine. Food Chem. 2005;93:507–10.10.1016/j.foodchem.2004.09.039Search in Google Scholar

[9] Zhu J, Hu B, Lu J, Xu S. Analysis of metabolites in Cabernet Sauvignon and shiraz dry red wines from Shanxi by 1H NMR spectroscopy combined with pattern recognition analysis. Open Chem. 2018;16:446–52.10.1515/chem-2018-0052Search in Google Scholar

[10] Sun SY, Che CY, Sun TF, Lv ZZ, He SX, Gu HN, et al. Evaluation of sequential inoculation of Saccharomyces cerevisiae and Oenococcusoeni strains on the chemical and aromatic profiles of cherry wines. Food Chem. 2013;138:2233–41.10.1016/j.foodchem.2012.12.032Search in Google Scholar PubMed

[11] Papotti G, Bertelli D, Graziosi R, Silvestri M, Bertacchini L, Durante C, et al. Application of one- and two-dimensional NMR spectroscopy for the characterization of protected designation of origin Lambrusco wines of Modena. J Agric Food Chem. 2013;61:1741–6.10.1021/jf302728bSearch in Google Scholar

[12] Bylesjö M, Rantalainen M, Cloarec O, Nicholson JK, Holmes E, Trygg J. OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. J Chemom. 2006;341–51.10.1002/cem.1006Search in Google Scholar

[13] Nicholson JK, Lindon JC, Holmes E. “Metabonomics”: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999;29:1181–9.10.1080/004982599238047Search in Google Scholar

[14] Anastasiadi M, Zira A, Magiatis P, Haroutounian SA, Skaltsounis AL, Mikros E. 1H NMR-based metabonomics for the classification of Greek wines according to variety, region, and vintage. Comparison with HPLC Data. J Agric Food Chem. 2009;57:11067–74.10.1021/jf902137eSearch in Google Scholar

[15] Lee JE, Hong YS, Lee CH. Characterization of fermentative behaviors of lactic acid bacteria in grape wines through 1H NMR- and GC-based metabolic profiling. J Agric Food Chem. 2009;57:4810–7.10.1021/jf900502aSearch in Google Scholar

[16] Trygg J, Wold S. Orthogonal projections to latent structures (O-PLS). J Chemom. 2002;16(3):119–28.10.1002/cem.695Search in Google Scholar

[17] Trygg J, Holmes E, Lundstedt T. Chemometrics inme tabo no mics. J Proteome Res. 2007;6:469–79.10.1021/pr060594qSearch in Google Scholar

[18] Romano P, Fiore C, Paraggio M, Caruso M, Capece A. Function of yeast species and strains in wine flavor. Int J Food Microbiol. 2003;86:169–80.10.1016/S0168-1605(03)00290-3Search in Google Scholar

[19] Radler F, Schütz H. Glycerol production of various strains of Saccharomyces. Am J Enol Vitic. 1982;33:36–40.10.5344/ajev.1982.33.1.36Search in Google Scholar

[20] Gardner N, Rodrigue N, Champagne CP. Combined effects of sulfites, temperature, and agitation time on production of glycerol in grape juice by Saccharomyces cerevisiae. Appl Env Microbiol. 1993;59:2022–8.10.1128/aem.59.7.2022-2028.1993Search in Google Scholar

[21] Rojas V, Gil JV, Piñaga F, Manzanares P. Acetate ester formation in wine by mixed cultures in laboratory fermentations. Int J Food Microbiol. 2003;86:181–8.10.1016/S0168-1605(03)00255-1Search in Google Scholar

[22] Viggiani L, Morelli MA. Characterization of wines by nuclear magnetic resonance: a work study on wines from the Basilicata region in Italy. J Agric Food Chem. 2008;56:8273–9.10.1021/jf801513uSearch in Google Scholar

[23] Avenoza A, Busto JH, Canal N, Peregrina JM. Time course of the evolution of malic and lactic acids in the alcoholic and malolactic fermentation of grape must by quantitative 1H NMR (qHNMR) spectroscopy. J Agric Food Chem. 2006;54:4715–20.10.1021/jf060778pSearch in Google Scholar

[24] Larsen FH, van den Berg F, Engelsen SB. An exploratory chemometric study of 1H NMR spectra of table wines. J Chemom. 2006;20:198–208.10.1002/cem.991Search in Google Scholar

[25] Košir IJ, Kidrič J. Use of modern nuclear magnetic resonance spectroscopy in wine analysis: determination of minor compounds. Anal Chim Acta. 2002;458:77–84.10.1016/S0003-2670(01)01549-5Search in Google Scholar

[26] Lee JE, Hwang GS, Van Den Berg F, Lee CH, Hong YS. Evidence of vintage effects on grape wines using 1H NMR-based metabolomic study. Anal Chim Acta. 2009;648:7–6.10.1016/j.aca.2009.06.039Search in Google Scholar PubMed

[27] Zeisel SH, Da Costa KA, Franklin PD, Alexander EA, Lamont JT, Sheard NF, et al. Choline, an essential nutrient for humans. FASEB J. 1991;5:2093–8.10.1096/fasebj.5.7.2010061Search in Google Scholar

[28] Mickelbart MV, Chapman P, Collier-Christian L. Endogenous levels and exogenous application of glycinebetaine to grapevines. Sci Hortic. 2006;111:7–16.10.1016/j.scienta.2006.07.031Search in Google Scholar

Received: 2020-05-20
Revised: 2021-01-28
Accepted: 2021-03-02
Published Online: 2021-03-17

© 2021 Shaochen Xu et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Regular Articles
  2. Qualitative and semi-quantitative assessment of anthocyanins in Tibetan hulless barley from different geographical locations by UPLC-QTOF-MS and their antioxidant capacities
  3. Effect of sodium chloride on the expression of genes involved in the salt tolerance of Bacillus sp. strain “SX4” isolated from salinized greenhouse soil
  4. GC-MS analysis of mango stem bark extracts (Mangifera indica L.), Haden variety. Possible contribution of volatile compounds to its health effects
  5. Influence of nanoscale-modified apatite-type calcium phosphates on the biofilm formation by pathogenic microorganisms
  6. Removal of paracetamol from aqueous solution by containment composites
  7. Investigating a human pesticide intoxication incident: The importance of robust analytical approaches
  8. Induction of apoptosis and cell cycle arrest by chloroform fraction of Juniperus phoenicea and chemical constituents analysis
  9. Recovery of γ-Fe2O3 from copper ore tailings by magnetization roasting and magnetic separation
  10. Effects of different extraction methods on antioxidant properties of blueberry anthocyanins
  11. Modeling the removal of methylene blue dye using a graphene oxide/TiO2/SiO2 nanocomposite under sunlight irradiation by intelligent system
  12. Antimicrobial and antioxidant activities of Cinnamomum cassia essential oil and its application in food preservation
  13. Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form
  14. Evaluation of the performance of immunoblot and immunodot techniques used to identify autoantibodies in patients with autoimmune diseases
  15. Computational studies by molecular docking of some antiviral drugs with COVID-19 receptors are an approach to medication for COVID-19
  16. Synthesis of amides and esters containing furan rings under microwave-assisted conditions
  17. Simultaneous removal efficiency of H2S and CO2 by high-gravity rotating packed bed: Experiments and simulation
  18. Design, synthesis, and biological activities of novel thiophene, pyrimidine, pyrazole, pyridine, coumarin and isoxazole: Dydrogesterone derivatives as antitumor agents
  19. Content and composition analysis of polysaccharides from Blaps rynchopetera and its macrophage phagocytic activity
  20. A new series of 2,4-thiazolidinediones endowed with potent aldose reductase inhibitory activity
  21. Assessing encapsulation of curcumin in cocoliposome: In vitro study
  22. Rare norisodinosterol derivatives from Xenia umbellata: Isolation and anti-proliferative activity
  23. Comparative study of antioxidant and anticancer activities and HPTLC quantification of rutin in white radish (Raphanus sativus L.) leaves and root extracts grown in Saudi Arabia
  24. Comparison of adsorption properties of commercial silica and rice husk ash (RHA) silica: A study by NIR spectroscopy
  25. Sodium borohydride (NaBH4) as a high-capacity material for next-generation sodium-ion capacitors
  26. Aroma components of tobacco powder from different producing areas based on gas chromatography ion mobility spectrometry
  27. The effects of salinity on changes in characteristics of soils collected in a saline region of the Mekong Delta, Vietnam
  28. Synthesis, properties, and activity of MoVTeNbO catalysts modified by zirconia-pillared clays in oxidative dehydrogenation of ethane
  29. Synthesis and crystal structure of N,N′-bis(4-chlorophenyl)thiourea N,N-dimethylformamide
  30. Quantitative analysis of volatile compounds of four Chinese traditional liquors by SPME-GC-MS and determination of total phenolic contents and antioxidant activities
  31. A novel separation method of the valuable components for activated clay production wastewater
  32. On ve-degree- and ev-degree-based topological properties of crystallographic structure of cuprite Cu2O
  33. Antihyperglycemic effect and phytochemical investigation of Rubia cordifolia (Indian Madder) leaves extract
  34. Microsphere molecularly imprinted solid-phase extraction for diazepam analysis using itaconic acid as a monomer in propanol
  35. A nitric oxide-releasing prodrug promotes apoptosis in human renal carcinoma cells: Involvement of reactive oxygen species
  36. Machine vision-based driving and feedback scheme for digital microfluidics system
  37. Study on the application of a steam-foam drive profile modification technology for heavy oil reservoir development
  38. Ni–Ru-containing mixed oxide-based composites as precursors for ethanol steam reforming catalysts: Effect of the synthesis methods on the structural and catalytic properties
  39. Preparation of composite soybean straw-based materials by LDHs modifying as a solid sorbent for removal of Pb(ii) from water samples
  40. Synthesis and spectral characterizations of vanadyl(ii) and chromium(iii) mixed ligand complexes containing metformin drug and glycine amino acid
  41. In vitro evaluation of lactic acid bacteria with probiotic activity isolated from local pickled leaf mustard from Wuwei in Anhui as substitutes for chemical synthetic additives
  42. Utilization and simulation of innovative new binuclear Co(ii), Ni(ii), Cu(ii), and Zn(ii) diimine Schiff base complexes in sterilization and coronavirus resistance (Covid-19)
  43. Phosphorylation of Pit-1 by cyclin-dependent kinase 5 at serine 126 is associated with cell proliferation and poor prognosis in prolactinomas
  44. Molecularly imprinted membrane for transport of urea, creatinine, and vitamin B12 as a hemodialysis candidate membrane
  45. Optimization of Murrayafoline A ethanol extraction process from the roots of Glycosmis stenocarpa, and evaluation of its Tumorigenesis inhibition activity on Hep-G2 cells
  46. Highly sensitive determination of α-lipoic acid in pharmaceuticals on a boron-doped diamond electrode
  47. Synthesis, chemo-informatics, and anticancer evaluation of fluorophenyl-isoxazole derivatives
  48. In vitro and in vivo investigation of polypharmacology of propolis extract as anticancer, antibacterial, anti-inflammatory, and chemical properties
  49. Topological indices of bipolar fuzzy incidence graph
  50. Preparation of Fe3O4@SiO2–ZnO catalyst and its catalytic synthesis of rosin glycol ester
  51. Construction of a new luminescent Cd(ii) compound for the detection of Fe3+ and treatment of Hepatitis B
  52. Investigation of bovine serum albumin aggregation upon exposure to silver(i) and copper(ii) metal ions using Zetasizer
  53. Discoloration of methylene blue at neutral pH by heterogeneous photo-Fenton-like reactions using crystalline and amorphous iron oxides
  54. Optimized extraction of polyphenols from leaves of Rosemary (Rosmarinus officinalis L.) grown in Lam Dong province, Vietnam, and evaluation of their antioxidant capacity
  55. Synthesis of novel thiourea-/urea-benzimidazole derivatives as anticancer agents
  56. Potency and selectivity indices of Myristica fragrans Houtt. mace chloroform extract against non-clinical and clinical human pathogens
  57. Simple modifications of nicotinic, isonicotinic, and 2,6-dichloroisonicotinic acids toward new weapons against plant diseases
  58. Synthesis, optical and structural characterisation of ZnS nanoparticles derived from Zn(ii) dithiocarbamate complexes
  59. Presence of short and cyclic peptides in Acacia and Ziziphus honeys may potentiate their medicinal values
  60. The role of vitamin D deficiency and elevated inflammatory biomarkers as risk factors for the progression of diabetic nephropathy in patients with type 2 diabetes mellitus
  61. Quantitative structure–activity relationship study on prolonged anticonvulsant activity of terpene derivatives in pentylenetetrazole test
  62. GADD45B induced the enhancing of cell viability and proliferation in radiotherapy and increased the radioresistance of HONE1 cells
  63. Cannabis sativa L. chemical compositions as potential plasmodium falciparum dihydrofolate reductase-thymidinesynthase enzyme inhibitors: An in silico study for drug development
  64. Dynamics of λ-cyhalothrin disappearance and expression of selected P450 genes in bees depending on the ambient temperature
  65. Identification of synthetic cannabinoid methyl 2-{[1-(cyclohexylmethyl)-1H-indol-3-yl] formamido}-3-methylbutanoate using modern mass spectrometry and nuclear magnetic resonance techniques
  66. Study on the speciation of arsenic in the genuine medicinal material honeysuckle
  67. Two Cu(ii)-based coordination polymers: Crystal structures and treatment activity on periodontitis
  68. Conversion of furfuryl alcohol to ethyl levulinate in the presence of mesoporous aluminosilicate catalyst
  69. Review Articles
  70. Hsien Wu and his major contributions to the chemical era of immunology
  71. Overview of the major classes of new psychoactive substances, psychoactive effects, analytical determination and conformational analysis of selected illegal drugs
  72. An overview of persistent organic pollutants along the coastal environment of Kuwait
  73. Mechanism underlying sevoflurane-induced protection in cerebral ischemia–reperfusion injury
  74. COVID-19 and SARS-CoV-2: Everything we know so far – A comprehensive review
  75. Challenge of diabetes mellitus and researchers’ contributions to its control
  76. Advances in the design and application of transition metal oxide-based supercapacitors
  77. Color and composition of beauty products formulated with lemongrass essential oil: Cosmetics formulation with lemongrass essential oil
  78. The structural chemistry of zinc(ii) and nickel(ii) dithiocarbamate complexes
  79. Bioprospecting for antituberculosis natural products – A review
  80. Recent progress in direct urea fuel cell
  81. Rapid Communications
  82. A comparative morphological study of titanium dioxide surface layer dental implants
  83. Changes in the antioxidative properties of honeys during their fermentation
  84. Erratum
  85. Erratum to “Corrosion study of copper in aqueous sulfuric acid solution in the presence of (2E,5E)-2,5-dibenzylidenecyclopentanone and (2E,5E)-bis[(4-dimethylamino)benzylidene]cyclopentanone: Experimental and theoretical study”
  86. Erratum to “Modified TDAE petroleum plasticiser”
  87. Corrigendum
  88. Corrigendum to “A nitric oxide-releasing prodrug promotes apoptosis in human renal carcinoma cells: Involvement of reactive oxygen species”
  89. Special Issue on 3rd IC3PE 2020
  90. Visible light-responsive photocatalyst of SnO2/rGO prepared using Pometia pinnata leaf extract
  91. Antihyperglycemic activity of Centella asiatica (L.) Urb. leaf ethanol extract SNEDDS in zebrafish (Danio rerio)
  92. Selection of oil extraction process from Chlorella species of microalgae by using multi-criteria decision analysis technique for biodiesel production
  93. Special Issue on the 14th Joint Conference of Chemistry (14JCC)
  94. Synthesis and in vitro cytotoxicity evaluation of isatin-pyrrole derivatives against HepG2 cell line
  95. CO2 gas separation using mixed matrix membranes based on polyethersulfone/MIL-100(Al)
  96. Effect of synthesis and activation methods on the character of CoMo/ultrastable Y-zeolite catalysts
  97. Special Issue on Electrochemical Amplified Sensors
  98. Enhancement of graphene oxide through β-cyclodextrin composite to sensitive analysis of an antidepressant: Sulpiride
  99. Investigation of the spectroelectrochemical behavior of quercetin isolated from Zanthoxylum bungeanum
  100. An electrochemical sensor for high sensitive determination of lysozyme based on the aptamer competition approach
  101. An improved non-enzymatic electrochemical sensor amplified with CuO nanostructures for sensitive determination of uric acid
  102. Special Issue on Applied Biochemistry and Biotechnology 2020
  103. Fast discrimination of avocado oil for different extracted methods using headspace-gas chromatography-ion mobility spectroscopy with PCA based on volatile organic compounds
  104. Effect of alkali bases on the synthesis of ZnO quantum dots
  105. Quality evaluation of Cabernet Sauvignon wines in different vintages by 1H nuclear magnetic resonance-based metabolomics
  106. Special Issue on the Joint Science Congress of Materials and Polymers (ISCMP 2019)
  107. Diatomaceous Earth: Characterization, thermal modification, and application
  108. Electrochemical determination of atenolol and propranolol using a carbon paste sensor modified with natural ilmenite
  109. Special Issue on the Conference of Energy, Fuels, Environment 2020
  110. Assessment of the mercury contamination of landfilled and recovered foundry waste – a case study
  111. Primary energy consumption in selected EU Countries compared to global trends
  112. Modified TDAE petroleum plasticiser
  113. Use of glycerol waste in lactic acid bacteria metabolism for the production of lactic acid: State of the art in Poland
  114. Topical Issue on Applications of Mathematics in Chemistry
  115. Theoretical study of energy, inertia and nullity of phenylene and anthracene
  116. Banhatti, revan and hyper-indices of silicon carbide Si2C3-III[n,m]
  117. Topical Issue on Agriculture
  118. Occurrence of mycotoxins in selected agricultural and commercial products available in eastern Poland
  119. Special Issue on Ethnobotanical, Phytochemical and Biological Investigation of Medicinal Plants
  120. Acute and repeated dose 60-day oral toxicity assessment of chemically characterized Berberis hispanica Boiss. and Reut in Wistar rats
  121. Phytochemical profile, in vitro antioxidant, and anti-protein denaturation activities of Curcuma longa L. rhizome and leaves
  122. Antiplasmodial potential of Eucalyptus obliqua leaf methanolic extract against Plasmodium vivax: An in vitro study
  123. Prunus padus L. bark as a functional promoting component in functional herbal infusions – cyclooxygenase-2 inhibitory, antioxidant, and antimicrobial effects
  124. Molecular and docking studies of tetramethoxy hydroxyflavone compound from Artemisia absinthium against carcinogens found in cigarette smoke
  125. Special Issue on the Joint Science Congress of Materials and Polymers (ISCMP 2020)
  126. Preparation of cypress (Cupressus sempervirens L.) essential oil loaded poly(lactic acid) nanofibers
  127. Influence of mica mineral on flame retardancy and mechanical properties of intumescent flame retardant polypropylene composites
  128. Production and characterization of thermoplastic elastomer foams based on the styrene–ethylene–butylene–styrene (SEBS) rubber and thermoplastic material
  129. Special Issue on Applied Chemistry in Agriculture and Food Science
  130. Impact of essential oils on the development of pathogens of the Fusarium genus and germination parameters of selected crops
  131. Yield, volume, quality, and reduction of biotic stress influenced by titanium application in oilseed rape, winter wheat, and maize cultivations
  132. Influence of potato variety on polyphenol profile composition and glycoalcaloid contents of potato juice
  133. Carryover effect of direct-fed microbial supplementation and early weaning on the growth performance and carcass characteristics of growing Najdi lambs
  134. Special Issue on Applied Biochemistry and Biotechnology (ABB 2021)
  135. The electrochemical redox mechanism and antioxidant activity of polyphenolic compounds based on inlaid multi-walled carbon nanotubes-modified graphite electrode
  136. Study of an adsorption method for trace mercury based on Bacillus subtilis
  137. Special Issue on The 1st Malaysia International Conference on Nanotechnology & Catalysis (MICNC2021)
  138. Mitigating membrane biofouling in biofuel cell system – A review
  139. Mechanical properties of polymeric biomaterials: Modified ePTFE using gamma irradiation
Downloaded on 8.1.2026 from https://www.degruyterbrill.com/document/doi/10.1515/chem-2020-0126/html
Scroll to top button