Grapevine (Vitis Vinifera L.), one of the most important cultivated fruit crops, is facing significant challenges due to climate change. Specifically, increasing temperatures negatively impact the physiological traits and disrupt plant phenology. Additionally, increased virulence in pathogen attacks and pests leads to significant yield loss, requiring widespread application of plant protection products. Traditional agronomic practices offer only partial mitigation, requiring the development of precise and effective intervention strategies. The economic worth of viticulture has prompted continuous efforts in grapevine genetic improvement programs, traditionally involving conventional breeding and clonal selection that, however, are complex and time-consuming approaches.

Oxidation processes constitute a main problem in winemaking. Oxidation result in color browning and varietal aroma loss, which are key attributes of wine organoleptic quality [1]. Despite the mechanisms involved in wine oxidation have been extensively reviewed [2], the protection of wine against oxidative spoilage remains one of the main goals of winemaking.
SO2 is one of the most efficient wine antioxidants used to prevent oxidation and microbial spoilage. However, intolerances caused by SO2 have led to the reduction of its concentration in 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.

Volatile thiols are impact aroma compounds, well-known in the literature for imparting tropical fruit aromas such as passion fruit, guava, grapefruit, and citrus in white wines [1]. More recent evidence suggests that tropical fruit aromas are also caused by other aroma compounds besides thiols, such as fermentation esters, or the interaction between these volatile families. Therefore, the objective of this study was to investigate the effects of combining esters and/or thiols to determine their impact on the fruitiness aroma perception of white wines. Pinot gris wine was produced at the OSU research winery and was dearomatized using Lichrolut® EN. Combinations of fermentation volatile compounds were added to the wine, forming the aroma base. Treatment wines were composed of additions of different concentrations and combinations of thiols and/or esters. Samples were subjected to sensory analysis where forty-six white wine consumers evaluated the orthonasal aroma of the wines and participated in Check-All-That-Apply (CATA).

Chitin is the main structural component of a large number of organisms (i.e., mollusks, insects, crustaceans, fungi, algae), and marine invertebrates including crabs and shrimps. The main derivative of chitin is chitosan (CH), produced by N-deacetylation of chitin in alkaline solutions. Over the past decade, the OIV/OENO 338A/ 2009 resolution approved the addition of allergen-free fungoid CH to must and wine as an adjuvant for microbiological control, prevention of haziness, metals chelation and ochratoxins removal (European Commission. 2011). Despite several studies on application of CH in winemaking, there are still very limited and controversial data on its interaction with acidic components in wine (Colan-gelo et al., 2018; Castro Marin et al., 2021).