Impact of climate change on the aroma of red wines: a focus on dried fruit aromas

Two transmittance-based chlorophyll meters (SPAD-502 and CCM-200) were evaluated in estimating chlorophyll (Chl) and nitrogen (N) levels in grapevine leaves.

AIM Terpene compounds are associated with floral notes and are characteristic of aromatic grape varieties such as Muscat (Jackson, 2008). They are generally considered to potentially contribute to the aroma of white wines. However, there is a growing interest towards the potential contribution of terpene compounds to the aroma of red wines. The aim of this work was to investigate the occurrence of different terpenes in red wines from Italian varieties. METHODS For this study wines from 11 mono-varietal Italian red wines from 12 regions were used (19 Sangiovese, 11 Nebbiolo, 10 Aglianico, 11 Primitivo, 10 Raboso del Piave, 9 Cannonau, 11 Teroldego, 3 Nerello, 9 Montepulciano, 7 Corvina). All samples were from vintage 2016 and none of them had been in contact with wood. A total of 19 terpenes and 7 norisoprenoids were analysed by mean of SPME-GC-MS analysis using a DVB-CAR-PDMS fiber. The wines were collected in the framework of the activities of the D-Wines (Diversity of Italian wines) project.

Does a white wine smell like red wine if you color it with red food coloring? A study by Morrot, Brochet, and Dubourdieu (2001, Brain and Language) suggests so. Subjects perceived red wine odors when tasting white wine that had been colored red. The perceived odor profile of the colored white wine became similar to that of a red wine. However, the forced-choice procedure used by Morrot et al. has some methodological shortcomings. Here, we used an alternative method (a rating procedure) to evaluate the presented wines.

The components and methodology for characterization of the terroir have been described by Gómez-Miguel & Sotés (1993-2014, 2003) and Gómez-Miguel (2011) taking into account the full range of environmental factors (i.e: climate, lithology, vegetation, topography, soils, altitude, etc.), landscape variables (derived from photo-interpretation and a digital elevation model), and specific variables to the country’s viticulture (i.e: size and distribution of the vineyards, varieties, phenology, productivity, quality, designation regulations, etc.).

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).