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. 2023 Jul 11;12(14):2668.
doi: 10.3390/foods12142668.

Differences in Aroma Profile of Cabernet Sauvignon Grapes and Wines from Four Plots in Jieshi Mountain Region of Eastern China

Zhuo Chen  1 , Yang-Peng Wu  1   2 , Yi-Bin Lan  1   2 , Yan-Zhi Cui  3 , Tong-Hua Shi  3 , Yu-Bo Hua  3 , Chang-Qing Duan  1   2 , Qiu-Hong Pan  1   2
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Differences in Aroma Profile of Cabernet Sauvignon Grapes and Wines from Four Plots in Jieshi Mountain Region of Eastern China

Zhuo Chen et al. Foods. .

Abstract

The Bohai Bay region is a famous wine-growing area in China, where the rainfall is concentrated in the summer due to the influence of the temperate semi-humid monsoon climate. As such, the vineyard terrain has a significant impact on the flavor quality of the grapes and the resulting wines. To explore the relationship between the 'Cabernet Sauvignon' wine style and terrain, this study takes four different plots in the Jieshi Mountain region to investigate the differences in the aroma profile of Cabernet Sauvignon grapes and wines of two consecutive vintages. Based on two-way ANOVA, there were 25 free and 8 glycosylated aroma compounds in the grapes and 21 and 10 aroma compounds with an odor activity value greater than 0.1 in the wines at the end of alcohol fermentation (AF) and malolactic fermentation (MLF), respectively, that varied among the four plots. Wines from the four plots showed a significant difference in floral and fruity aroma attributes, which were mainly related to esters with high odor activity values. The difference in concentration of these compounds between plots was more pronounced in 2021 than in 2020, and a similar result was shown on the Shannon-Wiener index, which represents wine aroma diversity. It has been suggested that high rainfall makes the plot effect more pronounced. Pearson's correlation analysis indicated that concentrations of (E)-3-hexen-1-ol in grapes and ethyl 3-methylbutanoate, ethyl hexanoate, isoamyl acetate, isopentanoic acid, and phenethyl acetate in wines were strongly positively correlated with the concentrations of N, P, K, Fe, and electrical conductivity in soil but negatively correlated with soil pH. This study laid a theoretical foundation for further improving the level of vineyard management and grape and wine quality in the Jieshi Mountain region.

Keywords: Cabernet Sauvignon; aroma; differences; plots; wines.

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Conflict of interest statement

Yan-Zhi Cui, Tong-Hua Shi and Yu-Bo Hua were employed by Bodega Longes Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The soil particle composition of four plots: 1, 2, and 3 represent depths of 0–30 cm, 30–60 cm, and 60–90 cm, respectively.
Figure 2
Figure 2
Heat maps for statistical difference in the concentration of aroma compounds between the plots in grapes. The components listed in this figure were shown via two-way ANOVA to be significantly influenced by plot across the two vintages (Tables S5 and S6). The concentrations were converted to log2 fold for standardization. The capital letters represent the differences between the four plots in 2020 determined via one-way ANOVA (p ≤ 0.05), and the lowercase letters represent the differences in 2021.
Figure 3
Figure 3
Pearson’s correlation coefficients between the concentration of aroma compounds in grapes and the physico-chemical properties of soil. The data from soils at a depth of 30–60 cm was used for analysis. 3-Methyl-1-butanol was the only bound aroma compound.
Figure 4
Figure 4
Heat maps for statistical difference in the concentration of aroma compounds between plots in wines. AF: Alcohol fermentation; MLF: Malolactic fermentation. The components listed in this figure were shown via two-way ANOVA to be significantly influenced by plot across two vintages (Tables S7 and S8). The concentrations were converted to log2 fold for standardization. The capital letters represented the differences between four plots in 2020 by one-way ANOVA (p ≤ 0.05) and the lowercase letters represented the differences in 2021.
Figure 5
Figure 5
Pearson’s correlation coefficients among the concentration of aroma compounds of wines and the physico-chemical properties of soil. Data from soils at a depth of 30–60 cm was used for analysis.
Figure 6
Figure 6
Aromatic categories calculated by adding the odor activity values of the compounds grouped in each one. (a) 2020; (b) 2021. The values were converted to log10 fold for visualization. * indicates that the values of one-way ANOVA (p ≤ 0.05) varied significantly between the four plots.
Figure 7
Figure 7
Sensory analysis results of wines after MLF in the year of 2020 and 2021. (a) 2020; (b) 2021.
Figure 8
Figure 8
Variation of Shannon–Wiener index of grape and wine aroma compounds in two vintages. Different letters indicate significant differences between plots in the same year (p < 0.05). * and *** indicate significant differences between vintages in the same plot at p < 0.05 and 0.001 based on one-way ANOVA, respectively. ns indicates no significance.
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