Targeted UHPLC-QqQ-MS/MS metabolomics for phenol identification in grapevine and wine: study of a Tempranillo clone with a dark-blue berry colour

Volatile organic compounds (VOCs) are emitted by nearly all plant organs of the plants, including leaves. They play a key role in the communication with other organisms, therefore they are involved in plant defence against phytopathogens. In this study VOCs from grapevine leaves of two varieties of Vitis vinifera infected by Erysiphe necator were analysed. The varieties were selected based on their susceptibility to pathogen, Kishmish Vatkana has the Ren1 resistance gene and Zamarrica showed high susceptibility in previous trials.

The concept of ‘terroir’ describes the interplay of the environmental factors that affect the grapevine. This includes but is not limited to climate, soil composition, vineyard management, topography, and geology.

The mechanical strength or firmness of a fruit is considered an important parameter to characterize its state of maturity or conservation, as other parameters such as sugar level or color.

Traditional agricultural and agro-pastoral systems (prior to industrial revolution) often have the characteristic of being multiple systems, in which multiple crops are hosted simultaneously on the same plot. currently research suggests to study more in depth the potential of multiple agricultural systems in order to detect those characteristics of multiple agrarian systems that could allow modern viticulture to adapt to the challenges posed by climate change: rising temperatures with impacts on the phenological cycle of the vine, resurgence of plant deseases, extreme soil washout phenomena and hail storms, among others.

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.