The interplay between grape ripening and weather anomalies – A modeling exercise

Wine complexity is considered a multidimensional yet equivocal sensory percept. This project uncovered sensory attributes Australian Chardonnay wine consumers associate with Chardonnay wine complexity
and correlations between expert and consumer perceived wine complexity and preference. A
wine consumer test examined 6 Australian Chardonnay wines of three complexity levels designated low (LC1&2), medium (MC1&2), and high (HC1&2) by an expert panel (n = 8) using a benchtop sensory task. Consumers (n = 81) rated their perceived liking using a 9-point hedonic scale; wine complexity with a 5-point scale anchored “low”, “low-medium”, “medium”, “medium-high”, and “high” and lastly, profiled the wines using Rate-All-That-Apply (RATA). Psychographic segmentation with the Fine Wine Instrument
(FWI) generated three segments; Wine Enthusiasts (WE n=29), Aspirants (ASP n=40) and No- Frills (NF n=12).

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.

The professional winegrower usually selects the harvest date considering several elements, such as the vine stem and berry colour, the flavour, appearance and grain elasticity. Nowadays these elements have turned old fashioned.

The interest in indigenous non-Saccharomyces yeast for use in wine production has increased in recent years because they contribute to the complex character of the wine. The aim of this work was to investigate the fermentation products of ten indigenous strains selected from a collection of native yeasts established at the Institute for Adriatic Crops and Karst Reclamation in 2021, previously isolated from Croatian Maraština grapes, belonging to Hypopichia pseudoburtonii, Metschnikowia pulcherrima, Metschnikowia sinensis, Metschnikowia chrysoperlae, Lachancea thermotolerans, Pichia kluyveri, Hanseniaspora uvarum, Hanseniaspora guillermondii, Hanseniaspora pseudoguillermondii, and Starmerella apicola species, and compare it with commercial non-Saccharomyces and Saccharomyces strains.

Corporate Social Responsibility (CSR), as defined by the European Commission, is a strategic framework through which companies integrate social, environmental, and economic sustainability into their operations (European Commission, 2001).