Potential deacidifying role of a commercial chitosan: impact on pH, titratable acidity, and organic acids in model solutions and white wine

High concentration of sugars in grapes and alcohol in wines is one of the consequences of climate change on viticulture production in several wine-growing regions. In order to investigate the possibilities of adaptation of vineyard management practices aimed to reduce the accumulation of sugar during the maturation phase without reducing the accumulation of anthocyanins in grapes, a study with severe shoot trimming, shoot thinning, cluster thinning and date of harvest was conducted on Merlot variety in Istria region (Croatia), under the Mediterranean climate. Four factors which may affect grape maturation and its composition at harvest were investigated in a two-years experiment; severe shoot trimming applied at veraison when >80% of berries changed colour (in comparison to untreated control), shoot thinning (0 and 30%), cluster thinning (0 and 30%), and the date of harvest (early and standard harvest dates). Shoot thinning had no significant impact on berry composition, despite the obtained reduction in yield per vine. Lower Brix in grapes were obtained with earlier harvest date and if no cluster thinning was applied, although at the same time a reduction in the concentration of anthocyanins in berries was observed in these treatments. On the other hand, if severe shoot trimming was applied when >80% of berries changed colour, a reduction of Brix was obtained without a negative impact on berry anthocyanins concentration. We conclude that in cases when undesirably high sugar concentrations at harvest are expected, severe shoot trimming at 80% veraison may effectively be used in order to obtain moderate sugar concentration in berries together with the adequate phenolic composition.

The classification of enological tannins has gained importance following the OIV’s requirement to include their botanical origin on product labels (OIV-OENO 624-2022).

In a highly competitive worldwide market, a current challenge for the beverage sector is to diversify the range of products and to offer wines and spirits with typicity and character.

During alcoholic fermentation, wine yeasts generate a large variety of volatile metabolites, including acetate esters, ethyl fatty acid esters, higher alcohols, volatile fatty acids and volatile sulfur compounds that contribute to the aroma profile of wine. These molecules, refered as fermentative aromas, are the most abundant volatile compounds synthetized by yeasts and the metabolic pathways involved in their formation have been well characterized. Furthermore, other molecules with a major organoleptic impact may be produced during wine fermentation including terpene derivatives. However, little information is available on the contribution of yeasts to the formation of these molecules, in particular on their ability to synthethise de novo the terpens derivatives or to produce hydrolytic enzymes involved in the release of varietal precursors.

Wine lees are quantitatively the second most important wine by-product after grape stems and marc [1]. In order to recycle, distilleries recovered ethanol and tartaric acid contained in wine lees but yeast biomass is often unused. It has already been demonstrated that this yeast biomass could be upcycled to produce yeast extracts of interest for wine chemical stabilization [2]. In addition, it is well known that lees, during aging, release compounds that preserve wine from oxidation.

Modern grapevine breeding programs have overcome many challenges using genomic selection, which has allowed breeders to make targeted selections at earlier stages in the breeding process. However, the cost of genetic testing may present a burden for some programs, and markers often struggle to accurately predict quantitative traits. Recent advances in high throughput, high-dimensional data have provoked investigation into the use of high-dimensional phenomics as a low-cost addition to the grape breeder’s toolkit that may offer advantages in predicting quantitative traits. High-dimensional secondary trait (HDST) data has been employed in annual crops for prediction of agriculturally important traits such as yield.