Predictive Modelling for Smart Vineyard Identity
- Landwirtschaftliche Produktion und Lebensmittel
Abstract
The final sensorial quality of a wine is the result of a multitude of interactions between all the chemical components within the wine and specific environmental factors such as the temperature of the wine. Since influenced by numerous factors such as grape varieties, growing conditions, climate change, yeast strains, wine making technologies, human experiences, the evaluation and preservation of wine quality – in terms of reproducibility from year to year - is nowadays the main challenge for both wine producers and wine science community. Viticultural practices aim primarily at producing high quality grapes that would reflect varietal flavours and aromas and/or characters typical for a specific region or terroir. In Austria, Districtus Austriae Controllatus (DAC) is a classification for regionally typical quality wine that provides products of distinction in wine market. An accurate evaluation and assessment of the wine quality, identity and typicity is of high significance for vintners to perform proper wine classification and target marketing. The aim of this project is on grape and wine quality evaluation, and regional typical quality characterization and prediction using elemental and sensory analysis, non-targeted and targeted metabolomics, spectroscopic approaches, and artificial intelligence. Grape quality is the most important factor for making high quality wine and some grape metabolites can have a strong relation to the wine quality. The relationship between the grape metabolites and the wine quality will be explored using non-targeted metabolomics and spectroscopic approaches and wine quality prediction models generated by artificial intelligence and machine learning algorithms. Of particular focus in this project is providing detailed chemical characterization that elucidates the influence of the Viennese wine growing region (origin) on Viennese Gemischter Satz DAC and Grüner Veltliner. As final output of the project, software, apps and a unique quality mark tag will be developed, for wine quality prediction and authenticity assessment based on established databases. This solution will be designed and developed to prove the identity and authenticity of each bottle and trace them. In turn, the outcomes of this project aim to both support origin marketing and future maintenance of wine production processes and wine quality in Vienna.
- Wine
- Non-targeted analysis
- Vinification
Project staff
Tim Causon
Assoc. Prof. PD Tim Causon B.Sc. Ph.D.
tim.causon@boku.ac.at
Tel: +43 1 47654-77101, 77187
BOKU Project Leader
01.06.2022 - 31.05.2025
Konrad Domig
Univ.Prof. Dipl.-Ing. Dr.nat.techn. Konrad Domig
konrad.domig@boku.ac.at
Tel: +43 1 47654-75453
Sub Projectleader
01.06.2022 - 31.05.2025
Stephan Hann
Univ.Prof. Dr. Stephan Hann
stephan.hann@boku.ac.at
Tel: +43 1 47654-77001, 77191
Sub Projectleader
01.06.2022 - 31.05.2025
Pegah Mousazadehfazeli
Pegah Mousazadehfazeli M.Sc.
pegah.mousazadehfazeli@boku.ac.at
Project Staff
15.06.2022 - 31.05.2025
BOKU partners
External partners
HBLVA und BA für Wein- und Obstbau
Ing. Dr. Reinhard Eder
partner
VinoStellar OG
Dr. Zora Jandric
partner