Afternoon shading delays ripening and modifies grape flavonoids and wine composition under natural heat stress in semi-arid regions

Abstract

To mitigate the rapid ripening by afternoon high temperature in semi-arid wine regions, grapevine artificial shading was applied in a three-year experiment (2021-2023) in China. The moderate shading (1.5 h, MD) and strong shading (2.5 h, SD) were applied in the afternoon. Climatic parameters were collected, grape and wine flavonoids were determined by HPLC-MS. Results revealed that shading reduced the berry surface temperature by up to 4 °C and delayed sugar accumulation, resulting in grapes with higher acidity (∼1 g/L) and lower Brix (∼0.5 °Brix) at harvest. MD and SD wines had lower flavonols, red hue, and anthocyanin derivates. Specific non-methylated anthocyanins and kaempferol-based flavonols were bioindicators of solar radiation. Wine flavor and aging chemistry were also investigated. Overall, afternoon shading delays grape ripening and regulates grape flavonoid profiles and thus modifies wine appearance and flavor. This study provides insights into regulating grape and wine quality in semi-arid regions.

Keywords: Bottle storage; Climate change; Light; Phenolic evolution; Solar radiation; Stress recovery.

Fig. 1

Light conditions of afternoon grapevine shading treatments and control in a day (a), the grapevine shading (b), and the randomized block design of the current study (c).

Fig. 2

The monthly mean temperature (a) and precipitation (b) of the experimental site, and the seasonal average photosynthetic active radiation (c), ambient temperature (d), and berry surface temperature (e) of grapevines in a day. Time of Day is expressed in Beijing Time (UTC+8); local solar time is approximately 2 hours earlier, with solar noon occurring around 14:00 Beijing Time.

Fig. 3

Physico-chemical parameters of grapes at harvest (a, b, c), and flavonoids in grape skin (d, e), and the whole berry (f). ‘*’ indicates significant differences between treatment and control (student's t-test at p < 0.05).

Fig. 4

The biplots of PLS-DA models based on grape flavonoids at harvest in three vintages, VIP values of biomarkers among treatments, and their relative abundance in three groups. Labels of compounds with a VIP lower than 1.0 were hidden in biplots of PLS-DA. Bold font indicates compounds that were identified as biomarkers in at least two vintages in the lollipop plot. A black border indicates a significant difference (Student's t-test at p < 0.05) in the heatmap. My, Myricetin; Kaem, Kaempferol; Qu, Quercetin; Sy, Syringetin; Cy, cyanidin; Dp, Delphinidin; Pn, Peonidin; Pt, Petunidin; glu, glucoside; ga, galactoside; rh, rhamnoside; ac, acetylglucoside; co, p-coumaroylglucoside; TMA, total monomeric anthocyanins; TFO, total flavonols.

Fig. 5

The color sheet of fresh wines under grapevine shading treatments in three vintages (a), and the OPLS-DA biplots based on the wine flavonoids in three vintages (b). The correlation analysis between grape flavonoids and wine flavonoids. In the biplot, labels of compounds with VIP lower than 1.0 were hidden. My, Myricetin; Kaem, Kaempferol; Qu, Quercetin; Sy, Syringetin; Dp, Delphinidin; Pn, Peonidin; Pt, Petunidin; glu, glucoside; ga, galactoside; rh, rhamnoside; ac, acetylglucoside; co, p-coumaroylglucoside; TMA, total monomeric anthocyanins; TFO, total flavonols. In the correlation heatmap, data were presented with the Pearson r value and the significant mark (*, p < 0.05, **, p < 0.01, ***, p < 0.001, student's t-test).

Fig. 6

Cluster analysis based on the grape flavonoids of 2021 (a, left) and 2022 (a, right) wines during bottle aging, the flavonoids labeled indicate the same trend in two vintages. Color sheet of 2021 (b, left) and 2022 (b, right) wines during bottle aging. Correlation analysis between CIELab index and wine phenolics (c).