The taste of color: how grape anthocyanin fractions affect in-mouth perceptions

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:

The main innovation in the VINIoT service is the joint use of two technologies that are currently used separately: vineyard monitoring using multispectral imaging and deployed terrain sensors. One part of the system is based on the development of artificial intelligence algorithms that are feed on the images of the multispectral camera and IoT sensors, high-level information on water stress, grape ripening status and the presence of diseases. In order to obtain algorithms to determine the state of ripening of the grapes and avoid losing information due to the diversity of the grape berries, it was decided to work along the first year 2020 at berry scale in the laboratory, during the second year at the cluster scale and on the last year at plot scale. Different varieties of white and red grapes were used; in the case of Galicia we worked with the white grape variety Treixadura and the red variety Mencía. During the 2020 and 2021 campaigns, multispectral images were taken in the visible and infrared range of: 1) sets of 100 grapes classifying them by means of densimetric baths, 2) individual bunches. The images taken with the laboratory analysis of the ripening stage were correlated. Technological maturity, pH, probable degree, malic acid content, tartaric acid content and parameters for assessing phenolic maturity, IPT, anthocyanin content were determined. It has been calculated for each single image the mean value of each spectral band (only taking into account the pixels of interest) and a correlation study of these values with laboratory data has been carried out. These studies are still provisional and it will be necessary to continue with them, jointly with the training of the machine learning algorithms. Processed data will allow to determine the sensitivity of the multispectral images and select bands of interest in maturation.

Making high quality but affordable Pinot noir (PN) wine is challenging in most terroirs and New Zealand’s (NZ) situation is no exception. To increase the probability of making highly typical PN wines producers choose to grow grapes in cool climates on lower fertility soils while adopting labour intensive practices. Stringent yield targets and higher input costs necessarily mean that PN wine cost is high, and profitability lower, in line-priced varietal wine ranges. To understand the reasons why higher yielding vines are perceived to produce wines of lower quality we have undertaken an extensive study of PN in NZ. Since 2018, we established a network of twelve trial sites in three NZ regions to find individual vines that produced acceptable commercial yields (above 2.5kg per vine) and wines of composition comparable to “Icon” labels. Approximately 20% of 660 grape lots (N = 135) were selected from within a narrow juice Total Soluble Solids (TSS) range and made into single vine wines under controlled conditions. Principal Component Analysis of the vine, berry, juice and wine parameters from three vintages found grape berry mass to be most effective clustering variable. As berry mass category decreased there was a systematic increase in the probability of higher berry red colour and total phenolics with a parallel increase in wine phenolics, changed aroma fraction and decreased juice amino acids. The influence of berry size on wine composition would appear stronger than the individual effects of vintage, region, vineyard or vine yield. Our observations support the hypothesis that it is possible to produce PN wines that fall within an “Icon” benchmark composition range at yields above 2.5kg per vine provided that the Leaf Area:Fruit Weight ratio is above 12cm2 per g, mean berry mass is below 1.2g and juice TSS is above 22°Brix.

Protected Designation of Origin “Jumilla” (PDO Jumilla) is located in the Spanish provinces of Albacete and Murcia, in the South-eastern part of the Iberian Peninsula, where most of the models predict a severe impact of climate change in next decades. PDO Jumilla covers an area of 247,054 hectares, of which more than 22,000 hectares

The first demonstration of the interest of carbon isotope composition of sugars in grapevine, as an integrated indicator of vineyard water status, dates back to 2000 (Gaudillère et al., 1999; Van Leeuwen et al., 2001). Thanks to the isotopic discrimination of Carbon that takes place during plant photosynthesis, under hydric stress conditions, it is possible to accurately estimate the photosynthetic activity. Ever since, δ13C has been widely applied with success to zonation, terroir studies and vine physiology research, but is still not widely used by viticulturists. This is quite astonishing by considering the impact of global warming on viticulture and the need to improve water management, that would justify a widespread use of δ13C.
The lack of private laboratories proposing the analysis, the cost of the technology, as well as the long analytical delays, have been detrimental to its development. Some laboratories tried to overcome the analytical difficulties of isotopic analysis by using fourier transformed infrared spectroscopy, as a fast and cheap alternative to the official OIV method (IRMS). These claimed FTIR models have never been published or peer reviewed and cannot be considered robust. In this work, thanks to the recent acquisition of IRMS technology, new modern and robust applications of δ13C for viticulture are proposed. This includes the use of the analysis to make parcel separations at harvesting, the possibility to increase the precision of hydric stress cartography and the potential cost reduction when compared with Scholander pressure bomb analysis.