REVINE project : regenerative agricultural approaches to improve ecosystem services in Mediterranean vineyards

AIM: Organic production of wine in the past years has known an important augmentation. This type of cultivation practice switches synthetic phytosanitary product for copper-based protection as fungicide.

AIM Durello is a sparkling wine produced in the Lessini mountains near Verona. The wine is made from Durella grapes, a native white grape variety with a particularly high acidity. In spite of the small production area (375 ha for only 35 producers), there is a growing interest in this product. However, little is known about the aromatic profiles of these wines. The aim of this work was to characterize the aroma profile of Durella base wines suitable for the production of Lessini Durello sparkling wine. METHODS 14 base wines from Durella grapesfrom different producers were used for this study. Solid Phase Microextraction (SPME) and Solid Phase Extraction (SPE) sampling techniques coupled to GC-MS analysis allowed to identify and quantify a total of 62 volatile compounds. RESULTS Durello base wines showed relatively high levels of vitispirane, ß-damascenone, ß-citronellol and esters.

The production of high-quality wines requires the use of high-quality grapes. Some compounds originating from grapes may negatively influence the odour and flavour of the resulting wine in their original form or as precursors for off-odours and –flavours. Therefore, a rapid evaluation of the grapes directly upon receival at the winery is advantageous. Up to now, grape aroma is mainly evaluated by tasting, however, this leads to subjective results. The use of near-infrared (NIR) spectroscopy allows a rapid, objective and destruction-free analysis without previous sample preparation. Moreover, the measurement can be integrated into an existing process without additional sampling.

In Enology the conventional processes of filtration for clarification and stabilization are giving place to alternative membrane processes, including nanofiltration (NF). Furthermore, the increased alcohol content in wines recorded in recent years became an important issue for all the main wine producing countries. Among techniques available to the wine industry to reduce the ethanol content, NF is certainly one of the newest. This study is focused on the evaluation of NF influence on wine physical-chemical composition, including mineral content, which in accordance to our best knowledge is a novelty.

The project VINIoT pursues the creation of a new technological vineyard monitoring service, which will allow companies in the wine sector in the SUDOE space to monitor plantations in real time and remotely at various levels of precision. The system is based on spectral images and an IoT architecture that allows assessing parameters of interest viticulture and the collection of data at a precise scale (level of grape, plant, plot or vineyard) will be designed. In France, three subjects were specifically developed: evaluation of maturity, of water stress, and detection of flavescence dorée. For the evaluation of maturity, it has been decided first to work at the berry scale in the laboratory, then at the bunch scale and finally in the vineyard. The acquisition of the spectral hyperstal image as well as the reference analyzes to measure the maturity, were carried out in the laboratory after harvesting the berries in a maturity monitoring context. This work focuses on a case study to predict sugar content of three different grape varieties: Syrah, Fer Servadou and Mauzac. A robust method called Roboost-PLSR, developed in the framework of this work (Courand et al., 2022), to improve prediction model performance was applied on spectra after the acquirement of hyperspectral images. Regarding the evaluation of water stress, to work with a significant variability in terms of water status, it has been worked first with potted plants under 2 different water regimes. The facilities have allowed the supervision of irrigation and micro-climatic conditions. The regression models on agronomic variables (stomatal conductance, water potential, …) are studied. To detect flavescence dorée, the experimental plan has consisted of work at leaf scale in the laboratory first, and then in the field. To detect the disease from hyper-spectral imaging, a combination of multivariate curve resolution-alternating least squares (MCR-ALS) and factorial discriminant analysis (FDA) was proposed. This strategy proved the potential towards the discrimination of healthy and infected leaves by flavescence dorée based on the use of hyperspectral images (Mas Garcia et al., 2021).