Altered lignans accumulation in a somatic variant of Tempranillo with increased extractability of polyphenols during winemaking

In viticulture, the challenges of local climate modelling are multiple: taking into account the local environment, fine temporal and spatial scales, reliable time series of climate data, ease of implementation and reproducibility of the method. At the local scale, recent studies have demonstrated the contribution of spatialization methods for ground-based climate observation data considering topographic factors such as altitude, slope, aspect, and geographic coordinates (Le Roux et al, 2017; De Rességuier et al, 2020). However, these studies have shown questions in terms of the reproducibility and sustainability of this type of climate study. In this context, we evaluated the potential of MODIS thermal satellite images validated with ground-based climate data (Morin et al, 2020). Previous studies have been encouraging, but questions remain to be explored at the regional scale, particularly in the dynamics of the massive use of bioclimatic indices to classify the climate of wine regions. The results at the local scale were encouraging, but this approach was tested in the current study at the regional scale. Several objectives were set: 1) to evaluate the downscaling method for land surface temperature time series, 2) to identify regional thermal structure variations. We used weekly minimum and maximum surface temperature time series acquired by MODIS satellites at a spatial resolution of 1000 m and downscaled at 500 m using topographical variables. Two types of analyses were performed:

Wine aroma is influenced by several viticultural and oenological factors. In this study we used experimental wine making in a full factorial design to determine the impact of grapevine age, must turbidity, and yeast strain on the aroma of Vitis vinifera L. cv. Riesling wines. A recently developed, non-targeted SPME-GC-MS fingerprinting approach for wine volatiles was used. This approach includes the segmentation and mathematical transformation of chromatograms in combination with Parallel Factor Analysis (PARAFAC) and subsequent deconvolution of important chromatogram segments.

Biodynamic agriculture, founded in 1924 by Rudolph Steiner, is a form of organic agriculture. Through a holistic approach, biodynamic agriculture seeks to preserve the diversity of agriculture and the existing interactions between the mineral world and the different components of the organic world. Biodynamic grape production involves the use of composts, herbal teas and mineral preparations such as 500, 501 and CBMT.
Several scientific studies have provided evidence on the effects of biodynamic farming on the soil, the plant and the wine. Numerous empirical opinions of wine growers support the existence of differences brought by such a management.

In 2022, wine production was estimated at around 260 million hl. This high production rate implies to generate a large amount of by-products, which include grape pomace, grape stalks and wine lees. It is estimated that processing 100 tons of grapes leads to ~ 22 tons of by-products from which ~ 6 tons are lees [1]. Wine lees are a sludge-looking material mostly made of dead and living yeast cells, yeast debris and other particles that precipitate at the bottom of wine tanks after alcoholic fermentation. Unlike grape pomace or grape stalks, few strategies have been proposed for the recovery and valorisation of wine less [2].

Wine quality maintenance during the storage is a fundamental aspect for both wine producers and consumers. Nowadays, great attention has been given to the light effect