Geologic and geomorphologic features applied for identification of wine terroir units by digital image processing, spectroradiometric and GIS techniques in Encruzilhada do Sul, RS, Brazil

The first demonstration of the interest of carbon isotope composition of sugars in grapevine, as an integrated indicator of vineyard water status, dates back to 2000 (Gaudillère et al., 1999; Van Leeuwen et al., 2001). Thanks to the isotopic discrimination of Carbon that takes place during plant photosynthesis, under hydric stress conditions, it is possible to accurately estimate the photosynthetic activity. Ever since, δ13C has been widely applied with success to zonation, terroir studies and vine physiology research, but is still not widely used by viticulturists. This is quite astonishing by considering the impact of global warming on viticulture and the need to improve water management, that would justify a widespread use of δ13C.
The lack of private laboratories proposing the analysis, the cost of the technology, as well as the long analytical delays, have been detrimental to its development. Some laboratories tried to overcome the analytical difficulties of isotopic analysis by using fourier transformed infrared spectroscopy, as a fast and cheap alternative to the official OIV method (IRMS). These claimed FTIR models have never been published or peer reviewed and cannot be considered robust. In this work, thanks to the recent acquisition of IRMS technology, new modern and robust applications of δ13C for viticulture are proposed. This includes the use of the analysis to make parcel separations at harvesting, the possibility to increase the precision of hydric stress cartography and the potential cost reduction when compared with Scholander pressure bomb analysis.

From the beginning of the yeast autolysis process, several interesting intracellular and cell wall constituyents are released to the media providing different characteristics to the wine, being this process extensively studied in sparkling wines due to their important contribution to their properties (1-2). Yeast derived products (YDs) try to emulate the natural yeast autolysis compounds release enhancing the organoleptic characteristics of resulting wines (2-3). This study is a comprehensive evaluation of the impact of the addition of different YDs added to base wine on the chemical, physical and sensory characteristics of the resulting sparkling wines. METHODS: Chardonnay base wine was employed to carry out this study. Three experimental YDs were added at 5 and 10 g/hL to the tirage liqueur: a yeast autolysate (YA), a yeast protein extract (PE) and an inactivated dry yeast from Torulaspora delbrueckii, (TD), and two commercial specific inactivated dry yeast: OPTIMUM WHITE® (OW) and PURE-LONGEVITY®(PL). After second fermentation, measurements were carried out after 3, 6, 9 and 18 months of aging on lees. General enological parameters, proteins, polysaccharides (HPLC-DAD-RID), volatile compounds profile (GC-MS), foaming characteristics (Mosalux), and descriptive sensory analyses were carried out.

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.

Saccharomyces cerevisiae, as the workhorse of alcoholic fermentation, is a major actor of winemaking. In this context, this yeast species uses alcoholic fermentation to convert sugars from the grape must into ethanol and CO2 with an outstanding efficiency: it reaches on average 92% of the maximum theoretical yield of conversion. Moreover, S. cerevisiae is also known for its great genetic diversity and plasticity that is directly related to its living environment, natural or technological and therefore to domestication. This leads to a great phenotypic diversity of metabolites production.

The balance between the different flavours of a wine largely determines its perception and appreciation by the consumers. In white wines, sweetness and sourness are usually the two poles balancing the taste properties. The bitter flavour, on the other hand, is frequently associated with a loss of equilibrium and all white wines (dry and sweet, young and aged) are affected.
Several bitter compounds are already well-described in wines.