Utilisation de données historiques pour caractériser le millésime en cours

Plants have evolved different strategies to cope with environmental stresses and, although still debated, it was observed that they can remember past stress occurrence. Anatomical and physiological adjustments have been observed in different grapevine cultivars after repeated drought exposure, however epigenetic, transcriptional and biochemical changes associated with drought-primed ecological memory have been poorly studied. This work was conceived to test whether exposure to recurring events of mild drought could prime vines to endure severe drought stress. Particularly, we investigated whether the expected improved stress tolerance of Vitis vinifera cv Nebbiolo plants subjected over years to moderate and long-lasting water stress events (WS-primed) depended on molecular memory phenomena or on resetting of stress-induced signals.

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.

Aims: The aims of this study were to i) characterize the impact of geographical origin and viticulture treatments on Chilean Cabernet-Sauvignon, and ii) develop machine learning models to predict its quality. 

Simgi® platform pursues the need for dynamic in vitro simulation of the human gastrointestinal tract optimized and adapted to food safety and health fields. The platform has confirmed the model’s suitability since its first’s studies with the consistency between the simulated colonic metabolism of wine polyphenols and the metabolic evolution observed with the intake of wine in human intervention studies [1]. 

The Valpolicella appellation, mainly known for Amarone and Ripasso, is experiencing growing interest in Valpolicella Superiore (VS), a lighter red wine aligning with consumer demand. However, anecdotal evidence suggests different stylistic interpretations of VS, potentially causing consumer confusion.