SKIN AND SEED EXTRACTS DIFFERENTLY BEHAVE TOWARDS SALIVARY PROTEINS

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.

From the beginning of the yeast autolysis process, several interesting intracellular and cell wall constituyents are released to the media providing different characteristics to the wine, being this process extensively studied in sparkling wines due to their important contribution to their properties (1-2). Yeast derived products (YDs) try to emulate the natural yeast autolysis compounds release enhancing the organoleptic characteristics of resulting wines (2-3). This study is a comprehensive evaluation of the impact of the addition of different YDs added to base wine on the chemical, physical and sensory characteristics of the resulting sparkling wines. METHODS: Chardonnay base wine was employed to carry out this study. Three experimental YDs were added at 5 and 10 g/hL to the tirage liqueur: a yeast autolysate (YA), a yeast protein extract (PE) and an inactivated dry yeast from Torulaspora delbrueckii, (TD), and two commercial specific inactivated dry yeast: OPTIMUM WHITE® (OW) and PURE-LONGEVITY®(PL). After second fermentation, measurements were carried out after 3, 6, 9 and 18 months of aging on lees. General enological parameters, proteins, polysaccharides (HPLC-DAD-RID), volatile compounds profile (GC-MS), foaming characteristics (Mosalux), and descriptive sensory analyses were carried out.

The European wine industry is facing multiple challenges derived from climate change and the pressure of different fungal diseases that are compromising the production of traditional varieties. A sustainable alternative maybe the adoption of resistant varieties.
In this study, we have evaluated the enological potential of 9 resistant varieties (5 white and 4 red varieties) in La Rioja. Microvinifications were carried out with three biological replications. Oenological parameters were very diverse with acid content varying from 2.6 g/L to 6.6 g/L.

The « Condado de Huelva » Registered Appellation Origin Mark (RAOM) is located in the Province of Huelva, in the southwest of Andalucía (Spain), being limited by the Atlantic Ocean and the Province of Sevilla. « Zalema », a white high productive grapevine plant is its major cultivar. The predominant rootstocks used are « Rupestris du Lot », « Castel 196-17 », « Couderc 161-49 », Couderc 33-09 », « Richter 110 » and « Millardet 41-B ». Traditionally, « Zalema » cv. has been dedicated to the elaboration of amber, bouquet-flavoured wines and in the last years mainly to young, fruit-flavoured white table wines.

A large varietal collection including over 1700 varieties was maintained in Conegliano, ITA, since the 1950s. Phenological data on a subset of 400 grape varieties including wine grapes, table grapes, and raisins were acquired at bud break, flowering, veraison, and ripening since 1964. Despite the efforts in maintaining and acquiring data over such an extensive collection, the data set has varying degrees of missing cases depending on the variety and the year. This is ubiquitous in phenology datasets with significant size and length. In this work, we evaluated four state-of-the-art methods to estimate missing values in this phenological series: k-Nearest Neighbour (kNN), Multivariate Imputation by Chained Equations (mice), MissForest, and Bidirectional Recurrent Imputation for Time Series (BRITS). For each phenological stage, we evaluated the performance of the methods in two ways. 1) On the full dataset, we randomly hold-out 10% of the true values for use as a test set and repeated the process 1000 times (Monte Carlo cross-validation). 2) On a reduced and almost complete subset of varieties, we varied the percentage of missing values from 10% to 70% by random deletion. In all cases, we evaluated the performance on the original values using normalized root mean squared error. For the full dataset we also obtained performance statistics by variety and by year. MissForest provided average errors of 17% (3 days) at budbreak, 14% (4 days) at flowering, 14.5% (7 days) at veraison, and 17% (3 days) at maturity. We completed the imputations of the Conegliano dataset, one of the world’s most extensive and varied phenological time series and a steppingstone for future climate change studies in grapes. The dataset is now ready for further analysis, and a rigorous evaluation of imputation errors is included.