One of the main challenges in the wine industry is to understand how different wine processing techniques and practices can influence the overall quality of the final product. Winemakers base the decision process mostly on their personal experience, which is often influenced by emotional aspects not always scientifically supported. Other issues come from the terroir and climate change, which are affecting the quality and production techniques, both in vineyards and wineries. In addition, it is important to consider that wine culture in the different production areas is also extremely variegated, even within the same country. Relying on analytical methods is a necessary step taken in many parts of the winemaking process, starting from the determination of the optimal time for the harvest. Monitoring the fermentation is usually performed by controlling the density or the residual sugars: secondary metabolites are usually not determined. This means that in some cases the fermentation can get stuck without really knowing the reason.
This research project aims to create a predictive multivariate statistical tool in order to support the winemaker during the workflow in the winery. So, the oenologist can obtain the desired style of wine by extracting information from correlating basic oenological parameters with high resolution and sensory analysis.
Pinot Noir cultivar is a very important variety for South Tyrol representing 9.1% of the local vineyard (source: vinialtoadige.com). The experimental scheme shown in figure 1 was developed in collaboration with a South Tyrolean winery. The study plan was aimed at ensuring control over the winemaking protocols while still working at the winery production scale (90 hL per experiment).
The experimental plan included four vineyards. Besides, for one of these vineyards, the plan included the study of a viticultural technique (treatment of the canopy with chitosan prior to harvest), and two different oenological treatments: pre fermentative 4-days cryo-maceration and 7-days grape freezing. The samples were analyzed by HS-SPME-GCxGC-ToF/MS for volatile compounds, HPLC-DAD-FLD for phenolic compounds with off-line HPLC-MS/MS to identify the components, and sensory analysis by quantitative descriptive analysis (QDA®) (Poggesi, et al., 2021). The study was repeated in two different vintages (2019 and 2020) with three replicates.
As a result, multivariate statistic models showed good separations between vineyards, frozen grapes, and the cryo-macerated treatment, and separation between chitosan treatment and the control treatment. Furthermore, the time evolution of the main chemical markers was evaluated. Finally, the results obtained on the 2019 vintage were supported by the 2020 ones.
Authors: Poggesi Simone1, Darnal1, Merkyte1, Longo1, Montali2 and Boselli1
1Faculty of Science and Technology, Free University of Bozen-Bolzano
2Faculty of Computer Science, Free University of Bozen-Bolzano
*corresponding author: firstname.lastname@example.org
Alto Adige Wine – Exquisite Wines from Northern Italy (altoadigewines.com)
Poggesi, S., de Matos, A. D., Longo, E., Chiotti, D., Pedri, U., Eisenstecken, D., Robatscher, P., & Boselli, E. (2021). Chemosensory profile of South Tyrolean pinot blanc wines: A multivariate regression approach. Molecules, 26(20), 1–18. https://doi.org/10.3390/molecules26206245
Keywords: Pinot Noir, bidimensional gas chromatography, non-volatile phenols, support decision tool, sensory analysis