Determination of quality related polyphenols in chilean wines by absorbance-transmission and fluorescence excitation emission matrix (a-teem) analyses

The most recent methodological developments in soil survey and land evaluation, that can be taken as reference in the viticultural field, go over usage of the GIS and database. These informatic tools, which begin to be widely utilised, consent to realise evaluations at different geographic scale and with different data quality and quantity in entrance.

The introduction into the Italian wine supply chain of the latest generation of fungi-resistant grapevine varieties, endowed with a greater or lesser strong resistance to downy and powdery mildews, represents a valid tool of making viticulture more sustainable, particularly in northern regions of the peninsula, where climatic conditions accentuate the pressure of fungal diseases. However, the affirmation of resistant varieties is a function of their agronomic value, as well as of their oenological and sensorial value. The purpose of this study was to evaluate in detail the sensory potential of the new resistant varieties, in order to understand their real possibility of inclusion in the modern global enological context.

Grapes from warm(ing) climates often contain excessive sugars but lack acidity. This can lead to highly alcoholic wines with compromised stability and balance. The yeast Lachancea thermotolerans can ameliorate such wines due to its metabolic peculiarity – partial fermentation of sugars to lactic acid. This study aimed to elucidate the population-wide diversity in L. thermotolerans, whilst selecting superior strains for wine sector. An extensive collection of isolates (~200) sourced from different habitats worldwide was first genotyped on 14 microsatellite loci. This revealed differentiation of L. thermotolerans genetic groups based on the isolation substrate and geography. The 94 genotyped strains were then characterised in Vitis vinifera cv. Chardonnay fermentations.

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.

Yield monitoring can provide the winegrowers with information for precise production inputs during the season, thereby, ensuring the best possible harvest. Yield estimation is currently achieved through an intensive process that is destructive and time-consuming. However, remote sensing provides a group of proximal technologies and techniques for a non-destructive and less time-consuming method for yield monitoring.The objective of this study was to analyse three different approaches, for measuring grapevine yield close to harvest.