Effect of nitrogen content on fermentation kinetics and aroma profile of assyrtiko wine

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).

Climate change establishes challenges, as well as opportunities for many sectors, and markedly the wine sector.

The sustainability certification ‘nachhaltig austria’ (www.sustainableaustria.com) has been offered to austrian wineries in an online version for 10 years and over 25% of the austrian wine-growing area is now certified. Since the 2022 harvest, ‘nachhaltig austria’ has automatically calculated the carbon footprint for each winery, per hectare of vineyard, per litre of bulk wine and per 0.75-litre bottle (poelz, w. And rosner, f.g. 2023). In last year’s publications and numerous presentations at national and international level, topics such as refilling glass bottles, lightweight glass bottles, renewable energy, … Etc.

Wine phenolic compounds are important secondary metabolites in enology due to their antioxidant and nutraceutical properties, and their role in the development of color, taste, and protection of wine from oxidation and spoilage. Tannins are valuable phenolic compounds that contribute significantly to these wine properties, especially in mouthfeel characteristics; however, tannin determination remains a significant challenge, with manual and time-consuming methods or complex methodologies. The purpose of this study is to propose a novel method for quantifying condensed tannins in finished wine products.

The application of sensors in viticulture is a fast and efficient method to monitor grapevine vegetative, yield and quality parameters and determine their spatial intra-vineyard variability. Molecular analysis at the gene expression level can further contribute to the understanding of the observed variability by elucidating how pathways responsible for different grape quality traits behave in zones diverging for one or the other parameter. The intra-vineyard variability of a Cabernet Sauvignon vineyard was evaluated by a standard Normalized Difference Vegetation Index (NDVI) mapping approach, employing UAV platform, accompanied by detailed ground-truthing (e.g. vegetative, yield, and berry ripening compositional parameters) that was applied in 14 spots in the vineyard. Berries from different spots were additionally investigated by microarray gene expression analysis, performed at five time points from fruit set to full ripening.