Composition and molar mass distribution of different must and wine colloids

Rotundone accumulation and biosynthesis is a complicated process. Previous research highlighted that these phenomenons were affected under ecophysiological conditions by viticultural practices (e.g. defoliation or irrigation). Individually, these practices often impact several abiotic factors that are difficult to separate such as temperature, water or nitrogen status, or radiation. Such dissociation can be achieved under controlled environmental conditions using potted vines.

The main objective of this study was to establish if beet sugar produces a different concentration of “fruity” volatile aroma compounds (VOCs), compared to cane sugar when used for second alcoholic fermentation of Auxerrois sparkling wines. Auxerrois base wine from the 2020 vintage was separated into two lots; half was fermented with cane sugar and half with beet sugar (both sucrose products and tested for sugar purity). These sugars were used in yeast acclimation (IOC 2007), and base wines for the second fermentation (12 bottles each). Base wines were manually bottled at the Cool Climate Oenology and Viticulture Institute (CCOVI) research winery. The standard chemical analysis took place at intervals of 0, 4 weeks, and 8 weeks post-bottling. Acidity and pH measurements were carried out by an auto-titrator. Residual Sugar (g/L) (glucose (g/L), fructose (g/L)), YAN (mg N/L), malic acid, and acetic acid (g/L) were analyzed by Megazyme assay kits. parameters were analyzed by Megazyme assay kits. Alcohol (% v/v) was assessed by GC-FID. VOC analysis of base wines, finished sparkling wines, as well as the two sugars in model sparkling wine solutions, was carried out by GC-MS. VOCs included ethyl octanoate, ethyl hexanoate, ethyl butanoate, ethyl decanoate, ethyl-2-methylbutyrate, ethyl-3-methylbutyrate, ethyl 2-methyl propanoate, ethyl 2- hydroxy propanoate, 1-hexanol, 2-phenylethan-1-ol, ethyl acetate, hexyl acetate, isoamyl acetate and 2-phenylethyl acetate.

The wine industry currently relies on a limited number of grapevine cultivars, comprised of numerous clones with slight differences in their viticultural, oenological, or stress-tolerance traits.

The need to rationalize agricultural inputs has recently increased interest in assessing vineyard variability in order to implement variable rate input applications, so-called ‘precision viticulture’. In many viticultural areas globally, precision viticulture is already widely used such as for selective harvesting and variable rate application (VRA) of inputs such as irrigation and/or fertilizer. Robust VRA relies on having a geostatistically accurate map (of one or more vineyard attributes) requiring high sampling densities, which can be cost- and time-prohibitive to obtain. Previous work on spatial interpolation using kriging have upscaled ground-based measurements, but such upscaling strategies are applicable only when vineyard conditions are spatially continuous and satisfies the assumption of second-order stationary processes. Alternatively, mixed models that combine kriging and auxiliary information, such as the regression kriging (RK) method, are more instructive for spatial predictions. In order to improve prediction accuracies, it is therefore necessary to incorporate additional information to achieve accurate spatial patterns with low error.

Oak wood has a significant impact on the chemical composition of wine, leading to transformations that influence its organoleptic properties, such as its aroma, structure, astringency, bitterness and color. Among the main extractible non-volatile polyphenol compounds released from oak wood, the ellagitannins are found [1].