Tuning the pH during the fermentation has a strong effect on the wine protein composition and the stability of the resulting white wines

A critical aspect of wine quality is the overall expression of wine flavour, which is formed by the interplay of volatile aroma compounds, their precursors, and taste and matrix components.
Grapes directly contribute to wine only a small number of potent aroma compounds, and the unique
sensory attributes and perceived quality of a wine result from combining 100s of metabolites of grapes, yeast and bacteria, and oak wood.

Aging on lees (AOL) is a powerful technique to protect varietal aroma and color. Simultaneously, helps to soften tannins and increase and improve wine body and structure. AOL is complementary to barrel aging modulating the wood impact and protecting wine from oxidative conditions.

The risk of wildfires is increasing as the frequency and severity of drought and heat waves continue to rise. Wildfires are associated with the combustion of plant materials and emit smoke. In the atmosphere, smoke may spread readily across large areas. Smoke is composed of solid and liquid phase particulates and gases and has been identified as a causal agent of “smoke taint” in wine. On a smoky day, the intensity of direct light decreases because these particulates scatter sunlight. Even though this effect is frequently assumed to decrease plant photosynthesis, this assumption ignores the potential changes in diffuse light and may be based on scant evidence.

The Muscadet area is situated in the southeast of Nantes, close to the Atlantic coast. It constitutes the western extension
of the French vineyard “Loire Valley”. The Muscadet is renowned and often spontaneously linked to a white wine.
However it remains misconceived as an ordinary wine, lacking authenticity.

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.