Modeling malic acid dynamics to ensure quality, aroma and freshness of Pinot blanc wines in South Tyrol (Italy)

Abstract

Pinot blanc is a leading grapevine variety in South Tyrol (Italy) for wine production. The high quality of its wines derives from a typical aroma of elegant apple notes and lively acidity. The typicity of the final wine depends on the origin of the vine, the soil, the oenological practices and time of harvest. The South Tyrolean mountainous areas meet the cold climatic requirements of Pinot blanc, which guarantee its sweet-acidic harmony obtained when organic acids are in balance with the other components of the wine. However, increasing temperatures in valley sites during the berry development period boost the activity of malic acid (MA) enzymes, which negatively affect the final sugar/acid ratio. Researchers are currently focused on understanding acid dynamics in wines, and there are no references for the best sugar/acid ratio for Pinot blanc. Moreover, the contribution of individual acids to the sensory profile of this wine has not yet been studied. In this study we address the effect of different climate conditions and site elevations on the sugar/acid ratio in developmental grapevine berries, and we evaluate the effect on wine bouquet. Even if different models and indices have been proposed for predicting sugar content, no predictive models exist for MA in white grapes. In a three-year study (2017, 2018 and 2019) that involved eight vineyards in four different location in South Tyrol at various elevations ranging from 223 to 730 m a.s.l., the relationships between bioclimatic indices, such as growing-degree day (GDD) and grapevine sugar ripeness (GSR) and grapevine berry content were investigated. The analysis reveals that GDD may potentially predict MA dynamics in Pinot blanc; hence, a GDD-based model was used to determine the GDD to reach target MA concentrations (3.5, 3.0, 2.5, 2.0 g/L). This simple model was improved with additional temperature-based parameters by feature selection, and the best three advanced models were selected and evaluated by 5-fold cross-validation. These models could be used to support location and harvest date choice to produce high-quality Pinot blanc wines.

Temperature-Based Grapevine Ripeness Modeling for cv. Touriga Nacional and Encruzado in the Dão Wine Region, Portugal

“…Jones and Davis [36] found a similar effect of precipitation during flowering and the ripening period on the sugar concentration of Cabernet Sauvignon and Merlot varieties. Michelini et al [28] concluded that the DD-based model to predict the malic acid dynamics improved its performance adding other temperature-based parameters.…”

“…Besides sugar content, the acid content of grape juice is also a critical quality parameter since it affects wine quality [2]. Berry sugar accumulation and acid degradation are influenced by climatic variables, such as air temperature [27,28]. The non-linear regression analyses of SC and TA with heat accumulation (HA) calculated using the select models demonstrated that a high fraction of SC and TA variance was explained by the variation of these temperature-based indices (Table S6).…”

“…The results highlight that the thermal conditions between flowering and the harvest had a lower influence on the variance of pH (adj R 2 = 0.56 for TN and adj R 2 = 0.63, for EN). Several studies also highlighted the key role played by thermal effects on these berry quality attributes during or at the end of the ripening period [12,23,28,[34][35][36][37]. On the other hand, by comparing the results of each plot, the relationships between these grape quality parameters and the thermal conditions are site dependent.…”

“…Therefore, the models able to forecast the evolution of berry quality attributes, as a complement of other tools used in ripening monitoring, could be of utmost relevance to the vintage date decision and to promote more efficient winery management. These models may also be an important tools to the cultivars classification [6,27] used to support cultivar choice in response to the climate change [28].…”

“…Based on a time series of sugar concentrations of 65 grapevine varieties, Parker et al [27] demonstrated that temperaturebased models could be used to predict the time to target sugar concentrations from 170 to 220 g/L. Michelini et al [28] demonstrated that the GDD-based model may be used to predict malic acid dynamics in cv. Pinot blanc and improved its accuracy with the addition of two other temperature-based parameters.…”