Time stability of visitors’ preferences for preserving the worldwide cultural landscape alto douro wine region

Consumer preference favors flint-glass wine bottles over the traditional dark-colored, but it is documented that light exposure can cause white wines to produce off-aromas and change in color, and consequently da[1]mage their quality. Aim of the study was to study the white wine shelf life under the typical supermarket conditions, by recording the light and temperature exposure, the colorimetric changes, and the light-strike fault. METHODS: One pilot experiment based on two white wines and eight-time points and one kinetic experiment based on four white wines and seven-time points were designed and realized using a typical supermarket shelf for 32 and 50 days, correspondently. By installing prototype sensors at 32 points of the shelf, the temperature, UV, IR, and Visible light exposure were registered every 10 min. Approximately 600 commercial wines, bottled in flint and colored glass, were used. The colorimetric changes of the wines were registered and the light-strike fault was evaluated.

The interaction between wine and landscapes is of an unsuspected richness. On the one side, the vineyards form part of the landscapes which they model. On the other side, the wines are related in their perception to the image of a region, a landscape and are at the origin of a cultural richness.

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.

The inherent variability of Nature poses challenges for researchers to draw clear conclusions from field experiments. Identifying and assessing adaptations to climate change requires agronomic field trials.

Climate change is leading to an increase in average temperature and in the severity and occurrence of heatwaves, and is already disrupting grapevine phenology. In Australia, with the evolution of the weather of grape growing regions that are already warm and hot, berry composition including flavonoids, for which biosynthesis depends on bunch microclimate, are expected to be impacted [1]. These compounds, such as anthocyanins and tannins, contribute substantially to grape and wine quality. The goal of this research was to determine how flavonoid extraction is impacted when bunches are exposed to high (>35 °C) and extreme high (>45 °C) temperatures during berry development and maturity.