H-NMR metabolic profiling of wines from three cultivars, three soil types and two contrasting vintages

The quality of wines has often been associated with their geographical area of production. The aim of this work was to characterize Protected Designation of Origin (PDO) Xinomavro red wines from different geographical areas of Amyndeon and Naoussa in Northern Greece, elaborated with variables that contribute to their differentiation, such as soil characteristics, altitude, monthly average temperature and rainfall. Xinomavro fruit parcels from different vineyards within the two PDO zones (5 PDO Naoussa and 6 PDO Amyndeon) were vinified following a standard winemaking process. A total of 25 aroma compounds were quantified using gas chromatography-mass spectrometry (GC-MS) with simultaneous full scan and selected ion monitoring for data recording, and odor activity values (OAVs) were determined.

There are yet many gaps in our knowledge about the aroma potential of winemaking grapes and its measurement. Trying to bring some light into this question, a new general strategy based on the accelerated hydrolysis of reconstituted phenolic and aromatic fractions (PAFs) extracted from grapes has been developed

AIM: Proteins in white wine can cause cloudiness or haze after bottling, which consumers may consider an indicator of poor quality. . As a consequence, winemakers often use bentonite, a clay-based material that binds protein, to remove proteins and achieve protein stabilisation. However, removing bentonite from wine after treatment can result in a 3-10% loss of wine (1)…

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 principles of virological control on different types of grapevine collections and plantations are summarized.