Effect of culture and familiarity on wine perception: a study with spanish and british wine experts

Eleven wine-growing sites are now on the UNESCO World Heritage List as Cultural Landscapes. If the viticultural character of these sites constitutes the main argument for the demonstration of their heritage value, the terroir and its biophysical and environmental characteristics tend however to appear in a minor mode compared to the aesthetic and cultural dimensions. In other words, the “specific characteristics of the soil, topography, climate, landscape and biodiversity” (OIV definition) are most often used as descriptive elements in the presentation of the sites, but it is more the aesthetic, historical,

Aims: The aims of this study were to (1) assess the response of the indigenous Cypriot variety Xynisteri to different irrigation regimes and (2) compare the performance of Xynisteri to Sauvignon Blanc grown in pots with different irrigation regimes.

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 vine is colonized by a multitude of micro-organisms (fungi, bacteria, oomycetes) mainly coming from the microbial reservoir constituted by the soil. These microorganisms have positive or negative effects on the vine (protection against pathogens, resistance to abiotic stress, nutrition, but also triggering of diseases) (Fournier, Pellan et al. 2022). In addition to these functional roles, they respond quickly to environmental changes (climate, cultural practices) which could make them good bioindicators of the functioning of the wine ecosystem.

Despite the extensive research performed during the last decades, the multifactorial mechanism responsible for the white wine protein haze formation is not fully characterized. Herein, a new model is proposed, which is based on the experimental identification of sulfur dioxide as a major modulating factor inducing wine protein haze upon heating. As opposed to other reducing agents, such as 2-mercaptoethanol, dithiothreitol and tris(2-carboxyethyl)phosphine hydrochloride (TCEP), the addition of SO2 to must/wine upon heating cleaves intraprotein disulfide bonds, hinders thiol-disulfide exchange during protein interactions and can lead to the formation of novel inter/intraprotein disulfide bonds. Those are eventually responsible for wine protein aggregation which follows a nucleation-growth kinetic model as shown by dynamic light scattering [1].