How a microscopic yeast makes a big difference – how geographic limitations of yeast populations can determine the regional aroma of wine

Irrigation is an important practice to influence vine quality, especially in Mediterranean regions, characterized by hot summers and severe droughts during the growing season. This study focused on deficit irrigation regime influence on amino acids composition of Monastrell grapevines under semiarid conditions (Albacete, Southeastern of Spain). In 2019, two treatments were applied: non-irrigation (NI) and regulated deficit irrigation (RDI), watered at 30% of the estimated crop evapotranspiration from fruit set to onset of veraison. Grape amino acids content was analyzed by HPLC. Berries from non-irrigated vines showed higher concentration of several amino acids, such as tryptophan (73%), arginine (70%), lysine (36%), isoleucine (27%), and leucine (21%), compared to RDI grapes. Arginine is, together with ammonium ion, the principal nitrogen source for yeasts during the alcoholic fermentation; while isoleucine, tryptophan, and leucine are precursors of fermentative volatile compounds, key compounds for wine quality. Moreover, NI treatment increased in a 14% the total amino acids content in grapes compared to RDI treatment. The reported effects might be because yield was 70% higher in RDI vines than in the NI ones and, therefore, the sink demand was increased in the irrigated vines. In addition, NI vines suffered more severe water stress and it is known that the amino acids synthesis and accumulation can be influenced by the plant response to stress. According to the results, the irrigation regime showed effect on amino acids concentration in Monastrell grapes under semiarid conditions. Grapes from non-irrigated vines showed a higher content of several amino acids relevant to the fermentative process and to the wine aroma compounds formation. It is demonstrated that the final content of nitrogen-related components in grapes is influenced by the irrigation regime. The convenience of the irrigation strategy to suggest will depend on the desired wine style and the target yield levels.

The planet is warmer than at any time in our recorded past and increasing greenhouse emissions and persistence in the climate system means that continued warming is highly likely. Climate change has already altered the basic framework of growing grapes for wine production worldwide and will likely continue to do so for years to come. The wine sector can continue to play an important role in leading the agricultural sector in addressing climate change. From developing on…

The aim of this study was to evaluate the effects of different soil types on the chemical composition of Mediterranean red wines, through untargeted and targeted 1H-NMR metabolomics. One milliliter of raw wine was analyzed by means of a Bruker Avance II 400 spectrometer operating at 400.15 MHz. The spectra were recorded by applying the NOESYGPPS1D pulse sequency, to achieve water and ethanol signals suppression. No modification of the pH was performed to avoid any chemical alteration of the matrix. The generation of input variables for untargeted analysis was done via bucketing the spectra. The resulting dataset was preprocessed prior to perform unsupervised PCA, by means of MetaboAnalyst web-based tool suite. The identification of compounds for the targeted analysis was performed by comparison to pure compounds spectra by means of SMA plug-in of MNova 14.2.3 software. The dataset containing the concentrations (%) of identified compounds was subjected to one-way analysis of variance (ANOVA) to highlight significant differences among the wines. The untargeted analysis, carried out through the PCA, revealed a clear differentiation among the wines. The fragments of the spectra contributing mostly to the separation were attributed to flavonoids, aroma compounds and amino acids. The targeted analysis leaded to the identification of 68 compounds, whose concentrations were significant different among the wines. The results were related to soils physical-chemical analysis and showed that: 1) high concentrations of flavan-3-ols and flavonols are correlated with high clay content in soils; 2) high concentrations of anthocyanins, amino acids, and aroma compounds are correlated with neutral and moderately alkaline soil pH; 3) low concentrations of flavonoids and aroma compounds are correlated with high soil organic matter content and acidic pH. The 1H-NMR metabolomic analysis proved to be an excellent tool to discriminate between wines originating from grapes grown on different soil types and revealed that soils in the Mediterranean area exert a strong impact on the chemical composition of the wines.

There is increasing scientific consensus that climate changeis the underlying cause of the prolonged dry and hot conditions that have increased the risk of extreme fire weather in many countries around the world. In December 2019, a bushfire event occurred in the Adelaide Hills, South Australia where 25,000 hectares were burnt and in vineyards and surrounding areas various degrees of scorching and infrastructure damage occurred. The ability to coordinate and plan recovery after a fire event relies on robust and timely data. The current practice for measuring the scale and distribution of fire damage is to walk or drive the vineyard and score individual vines based on visual observation. The process is time consuming, subjective, or semi-quantitative at best. After the December 2019 fires, it took many months to access properties and estimate the area of vineyard damaged. This study compares the rapid assessment and mapping of fire damage using high-resolution satellite imagery with more traditional ground based measures. Satellite imagery tracking vineyard recovery in the season following the bushfire is being correlated to field assessments of vineyard productivity such as canopy health and development, fertility and carbohydrate storage. Canopy health in the seasons following the fires correlated to the severity of the initial fire damage. Severely damaged vines had reduced canopy growth, were infertile or had very low fertility as well as lower carbohydrate levels in buds and canes during dormancy, which reduced productivity in the seasons following the bushfire event. In contrast, vines that received minor damage were able to recover within 1-2 years. Tools that rapidly and affordably capture the extent and severity of damage over large vineyard area will allow producers, government and industry bodies to manage decisions in relation to fire recovery planning, coordination and delivery, improving the efficiency and effectiveness of their response.

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.