Evaluation of the sensory profile of doc douro red wines through sensory traditional single-point techniques and temporal dominance methods

Grafting plants uses intrinsic healing processes to join two different plants together to create one functional organism. To further our understanding of the molecular changes occurring during graft union formation in grapevine, we characterized the metabolome and transcriptome of intact and wounded cuttings (with and without buds to represent scions and rootstocks respectively), and homo- and heterografts at 0 and 14 days after wounding/grafting. As over-wintering, dormant plant material was grafted, we also characterized the gene expression changes in the wood during bud burst and spring activation of growth. We observed an asymmetrical pattern of gene expression between above and below the graft interface, auxin and sugar related genes were up-regulated above the graft interface, while genes involved in stress responses were up-regulated below the graft interface.

To assess the relationship between the reported low-stability of Tannat colour during wine storage and its pigment composition and evolution

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.

Nuclear magnetic resonance spectroscopy (NMR) is a high-tech analytical method that recently found its way into the field of wine analysis with special focus on wine authentication.

Pinking of white wine is an undesired change potentially occurring over storage, leading to the turning of color from yellow into salmon-red hue.