Terroir valorization strategies in a reformed denomination area: the Prosecco case study

Saccharomyces cerevisiae (SC) is the main driver of alcoholic fermentation however, in wine, non-Saccharomyces species can have a powerful effect on aroma and flavor formation. This study aimed to compare untargeted volatile compound profiles from SPME-GC×GC-TOF-MS of Sauvignon blanc and Shiraz wine inoculated with six different non-Saccharomyces yeasts followed by SC. Torulaspora delbrueckii (TD), Lachancea thermotolerans (LT), Pichia kluyveri (PK) and Metschnikowia pulcherrima (MP) were commercial starter strains, while Candida zemplinina (CZ) and Kazachstania aerobia (KA), were isolated from wine grape environments. Each fermentation produced a distinct chemical profile that was unique for both grape musts. The SC-monoculture and CZ-SC sequential fermentations were the most distinctly different in the Sauvignon blanc while the LT-SC sequential fermentations were the most different from the control in the Shiraz fermentations.

Context and purpose of the study. The wine industries face significant challenges from two highly detrimental viruses: leafroll and red blotch.

The present paper reports the development of an optimized method for simultaneous analysis of
8 biogenic amines (Histamine, Methylamine, Ethylamine, Tyramine, Putrescine, Cadaverine, Phenethylamine, and Isoamylamine). It is based on a method developed by Gomez-Alonso et al. in 2007.

he main goal of this research was to identify key aroma compounds linked with the maturity of grapes (ripe and overripe) and involved in grapes and wines with an intense dried fruits aroma. Odoriferous zones reminiscent of these aromas were detected by gas chromatography coupled with olfactometry (GC-O).

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.