Development of a new method to understand headspace aroma distribution and explore the pre-sensory level in perceptive interactions involved in red wine fruity aroma expression

Caffeic acid (CA) is an antioxidant of great importance in the food sector, such as wine, where it acts as a marker of wine ageing, as well as in the health sector due to its antioxidant properties and beneficial effects including the prevention of inflammation, cancer, neurodegenerative diseases, and diabetes.

Wine quality is a complex concept determined by multiple factors, including vineyard management, winemaking operations, and the sensory perception of key attributes.

Wine quality is influenced by grape maturity, typically monitored by measuring sugar content and acidity.

1,1,6-trimethyl-1,2-dihydronaphtelene (TDN) evokes the odor of “petrol” in wine, especially in the variety Riesling. Increasing UV-radiation due to climate change intensifies formation of carotenoids in the berry skins and an increase of TDN-precursors1. Exploring new viticultural and oenological strategies to limit TDN formation in the future requires precise knowledge of TDN thresholds in different matrices. Thresholds reported in the literature vary substantially between 2 µg/L up to 20 µg/L2,3,4 due to the use of different methods. As Riesling grapes are used for very different wine styles such as dry, sweet or sparkling wines, it is essential to study the impact of varying ethanol and carbonation levels.

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: