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.