Rare earth elements distribution in grape berries

Satellite images are used to determine the reflectance dependency to wavelength in different grape varieties (Cabernet-Sauvignon, Merlot, Pinot Noir, and Chardonnay). The terroir influence is investigated through study of vineyards in France, Brazil and Chile.

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.

Wine production is a complex multi-step process and the end-product is not easily defined in terms of composition and quality due to the diversity of the raw materials (grapes) and the biological agents (yeast and bacteria) used/present during the fermentation. Furthermore, linking what happens in the vineyard to the wine fermentation and ultimately to characteristics in the wine during ageing

Consumer expectations are increasingly shifting towards “residue-free wines.” However, from an analytical standpoint, “zero” does not exist. Laboratories often use the quantification limits of analysis methods to signify ‘zero.’ Improved techniques now allow for the quantification of levels that were previously undetectable. This is why we prefer to use the term “unquantifiable residue” rather than “absence of residues.”

When someone watches a hilly landscape, the image beauty creates emotions and frames of mind not easily forgettable, but sometimes man’s intervention by means of soil movement and reduction of the natural biodiversity can significantly modify the landscape and consequently the above-mentioned emotions. One speculates if sensory appreciation of a wine may be strongly affected by psychological factor: landscape beauty.