Variety “Rebula” (Vitis vinifera L.) determines the terroir Goriška brda “Collio” in Slovenia

In questa nota viene analizzata l’importanza della conoscenza del territorio nel funzionamento del mercato dei prodotti alimentari di qualità e nella gestione delle denominazioni di origine.
La denominazione di origine si sta affermando in tutti i mercati alimentari, dopo l’esperienza secolare maturata nel mercato del vino. Iniziative nel campo del turismo, delle produzioni ecologiche, della promozione dello sviluppo, sono collegate alla dimensione territoriale, in risposta ad un generale orientamento della domanda.

A new framework for the segmentation and characterization of row crops on remote sensing images has been developed and validated for vineyard monitoring. This framework operates on any high-resolution remote sensing images since it is mainly based on geometric information. It aims at obtaining maps describing the variation of a vegetation index such as NDVI along each row of a parcel.

The vinification by Carbonic maceration (CM) involves the process whereby the whole bunches are subjected to anaerobic conditions during several days. In this anaerobic condition, the grape endogenous enzymes begin an intracellular fermentation. This situation favors that whole grapes split open and release their juice into the tank, increasing the liquid phase that is fermented by yeasts [1]. Then, two types of wines are obtained; one from the free-run liquid in the tank (FCM) and other from the liquid after pressing the whole grape bunches (PCM). PCM wines are recognized as high quality young wines because their fruity and floral aromas[2] that although they are very intense at the end of the winemaking they gradually disappear during conservation.

The main goal of the current study is the development of a cost-effective and easy-to-use method suitable for use in the laboratory of commercial wineries to analyze wine aroma. Additionally, this study attempted to establish a prediction model for wine quality gradings based on their aroma, which could reveal the important aroma compounds that correlate well with different grades of perceived quality METHODS: Parameters of the SHS−GC−IMS instrument were first optimized to acquire the most desirable chromatographic resolution and signal intensities. Method stability was then exhibited by repeatability and reproducibility. Subsequently, compound identification was conducted. After method development, a total of 143 end-ferment wine samples of three different quality gradings from vintage 2020 were analyzed with the SHS−GC−IMS instrument. Six machine learning methods were employed to process the results and construct a quality prediction model. Techniques that aim to explain the model to extract useful insights were also applied.

The purpose of the study was to monitor berry development as a function of site, vine water status and climate in order to improve our understanding of the role played by climate change on secondary metabolites relevant to wine quality.