Effect of “Terroir” on quanti-qualitative paramethers of “vino nobile di Montepulciano”

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.

AIM: The composition and quality of wine are directly linked to microorganisms involved in the alcoholic fermentation. Several studies have been conducted on the impact of Saccharomyces cerevisiae on volatile compounds composition after fermentation. However, if different studies have dealt with combined sensory and volatiles analyses, few works have compared so far the impact of distinct yeast strains on the global metabolome of the wine.

Copper in white wine can be associated with Cu(II) organic acids (Cu fraction I), Cu(I) thiol species (Cu fraction II), and Cu sulfides (Cu fraction III). The first two fractions are associated with the repression of reductive aromas in white wine, but these fractions gradually decrease in concentration during the normal bottle aging of wine. Although exposure of white wine to fluorescent light is known to induce the accumulation of volatile sulfur compounds, causing light-struck aroma, the influence on the loss of protective Cu fractions is uncertain. Riboflavin is known to be a critical initiator of photochemical reac-tions in wine, but the rate of its decay under short-term light exposure in different coloured bottles and for wine of different oxygen concentrations is not well understood.

Acetaldehyde is a common component of wine. It is already formed during the fermentation being an intermediate in the production of ethanol. Moreover, it can derive from the oxidation of ethanol during the wine production and aging. In wine, concentrations of acetaldehyde range from 30 to 130 mg/L. Acetaldehyde in wine can react with many compounds such as SO2, amino acids and

Bacillus velezensis and Trichoderma harzianum are relevant microorganisms used in viticulture as biocontrol agents against pathogens of trunk (e.g. Phaeoacremonium minimum), leaves (e.g. Plasmopara viticola) or fruit (e.g. Botrytis cinerea), or as biostimulants, improving the resilience of plants against biotic or abiotic stressors through different direct and non-direct interactions.
In this biotechnological approach, formulation plays a crucial role. Controlling water activity in the product, thus stabilising microbial viability is key to ensuring effective application. We present the benefits of the citrate ester CITROFOL® AI (triethyl citrate) as a novel bio-based carrier liquid in microbial formulations. CITROFOL® AI is safe for humans and the environment, thus offering a promising base for sustainable treatments in viticulture.