Abstract
Pinot Blanc, an important grape variety grown in some mountain areas of Northern Italy such as South Tyrol over the last decades, with its cultivation covering 10.3% of the total vineyards, has compatible climatic conditions (e.g. heat requirements) which are normally found in the geographical areas of the mountain viticulture [1,2,3,4]. Climatic changes are hastening the growth of this variety at higher elevations, particularly for the production of high quality wine. This report illustrates an example of a collaboration study where data were collected in a commercial winemaking setting to look into the factors that contribute to Pinot Blanc’s typicity. The Control samples used a similar conventional vinification to compare three vineyards (Aldino, Montagna and Klaus). Four distinct winemaking techniques were examined for the vineyard “Aldino” taking into consideration characteristics like pre-fermentative grape freezing and co-inoculation with lactic acid bacteria. Musts before inoculation, young wines after one month and four month of aging and bottled wines at 0, 6 and 12 months of storage were investigated. The samples were analyzed by an offline HPLC-MS for the determination of the phenolic compounds and by HS-SPME-GCxGC-ToF/MS for determining the volatile profiles. The sensory analysis of the bottled wines was performed using Quantitative Descriptive Analysis (QDA ®) [5]. The profile of phenolic and volatile compounds of both musts and young wines were peculiar of each vineyards. For Aldino vineyard, the main differentiating factor for the musts and the young wines was the pre-fermentative grape freezing. No clear difference was observed in the phenolic and volatile profile as a function of co-inoculation with malolactic bacteria. For the bottled wines, specific sensory attributes contributed in the separation of the vineyards at all storage times. Furthermore, the overall quality judgement (OQJ) was significantly higher in all the wines at T12 storage time and for wines from Aldino. The sensory data were also combined with the chemical results to build multivariate models that exemplify how the components affect the wine’s final quality. These built models as fingerprint databases could provide assistance to the winemakers during production and also render useful for authenticity purposes.