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. 2022 Aug 16:13:978497.
doi: 10.3389/fpls.2022.978497. eCollection 2022.

The combined influence of rootstock and vintage climate on the grape and wine flavonoids of Vitis vinifera L. cv. Cabernet Sauvignon in eastern China

Affiliations

The combined influence of rootstock and vintage climate on the grape and wine flavonoids of Vitis vinifera L. cv. Cabernet Sauvignon in eastern China

Xiao Han et al. Front Plant Sci. .

Abstract

Rootstocks are commonly utilized owing to their resistance to abiotic and biotic stress in viticulture. This study evaluated the effects of three rootstocks (1103P, SO4, and 5A) on the Cabernet Sauvignon (CS) vine growth, and their berries and wines flavonoids profiles in four consecutive vintages. The results showed that 1103P increased the pruning weight of CS and decreased the anthocyanin concentration in berries and wines, especially in the vintages with more rainy and cloudy days. 5A tended to decrease the pruning weight of CS and increase the anthocyanin concentration in berries and wines. Orthogonal partial least squares discriminant analysis (OPLS-DA) showed that the concentrations of total anthocyanins, F3'H-anthocyanins, malvidin-3-O-glucoside (Mv-glu), and malvidin-3-O-acetylglucoside (Mv-acglu) were the key substances affected by the rootstocks in CS berries and were significantly decreased by 1103P. Total anthocyanins, pinotins, Mv-glu, epicatechin, and vitisins were the rootstock-sensitive compounds that commonly differed in wines among the three comparison groups in the two vintages. Furthermore, 1103P brought more brightness to the wine and 5A gave the wine more red tones. In conclusion, rootstock 5A was recommended in the rainy and cloudy climate regions with regard to the berry flavonoids accumulation and the wine color.

Keywords: Cabernet Sauvignon; OPLS-DA; color; flavonoids; rootstock; wine.

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Conflict of interest statement

The 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
Texture of vineyard soil (A) and PCA based on meteorological data in 2017–2020 (B).
Figure 2
Figure 2
Principal component analysis (PCA) based on flavonoid compound concentrations of grapes in four vintages (2017–2020) (A); Orthogonal partial least squares discriminant analysis (OPLS-DA) based on flavonoid compound concentrations of grapes in three vintages (2018–2020) (B–D); Venn diagram based on differential compounds (VIP > 1) in grapes (E). The abbreviated names of the substances in the figure are shown in Supplementary Table S6.
Figure 3
Figure 3
OPLS-DA based on flavonoid compound concentrations in wine of CS and CS/5A in the 2019 and 2020 growing seasons.
Figure 4
Figure 4
Principal component analysis (PCA) based on flavonoid compound concentrations of wine in two vintages (2019–2020) (A); OPLS-DA based on flavonoid compound concentrations of wine in two vintages (2019–2020) (B–D); Venn diagram based on differential compounds (VIP > 1) in wines (E). The abbreviated names of the substances in the figure are shown in Supplementary Table S7.
Figure 5
Figure 5
Partial least squares regression (PLSR) analysis for all wines based on CIELAB parameters and flavonoid compounds (A); PSLR analysis for grapes based on flavonoid compounds and environmental factors (B); PSLR analysis for all wines based on key flavonoid compounds affecting wine color parameters and flavonoids in grapes (C). The abbreviated names of the substances in the figure are shown in Supplementary Tables S6, S7.

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