Neural networks and ft-ir spectroscopy for the discrimination of single varietal and blended wines. A preliminary study.

In some white wines, malolactic fermentation (MLF) is very interesting, and for low pH wines this process is particularly difficult. Although MLF is generally not recommended for sparkling white wine, some winemakers prefer to promote MLF to contribute to organoleptic complexity. Oenococcus oeni is generally the bacterium of choice for MLF.

Sterols are a class of the eukaryotic lipidome that is essential for the maintenance of the cell membrane integrity and their good functionality (Daum et al., 1998).

In a previous work1, it was suggested that the different contents in delphinidin and catechin of the grapes were determinant on the O2 consumption and Strecker aldehyde (SAs) accumulation rates. Higher delphinidin seemed to be related to a faster O2 consumption and a smaller SAs accumulation rate, and the opposite was observed regarding catechin.
In the present paper, these observations were fully corroborated by adding synthetic delphinidin to a wine model containing polyphenolic fractions (PFs) extracted from garnacha and synthetic catechin to a wine model containing PF extracted from tempranillo: The delphinin-containing garnacha model consumed O₂ significantly faster and accumulated significantly smaller amounts of SAs than the original garnacha model, and the catechin-containing tempranillo model, consumed O2 significantly slower and accumulated significantly higher amounts of SAs than the original tempranillo model.

In viticulture, a warming climate can have a very significant impact on grapevine development and therefore on the quality and characteristics of wines across different spatial scales, ranging from global to local. In order to adapt wine-growing to climate change, global climate models can be used to define future scenarios, but only at the scale of major wine regions. Despite the huge progress made over the last ten years in terms of the spatial resolution of climate models (now downscaled to a few square kilometres), they are not yet sufficiently precise to account for the local climate variability associated with such parameters as local topography, in spite of these parameters being decisive for vine and wine characteristics. This study describes a method to downscale future climate scenarios to vineyard scale. Networks of data loggers have been used to collect air temperature at canopy level in the Waipara winegrowing region (New Zealand) over five growing seasons. These measurements allow the creation of fine-scale geostatistical models and maps of temperature (at 100 m resolution) for the growing season. In order to model climate change at pilot site scale, these geostatistical models have been combined with regional climate change predictions for the periods 2031-2050 and 2081-2100 based on the RCP8.5 climate change scenario. The integration of local climate variability with regionalized climate change simulations allows assessment of the impacts of climate change at the vineyard scale. The improved knowledge gained using this methodology results from the increased horizontal resolution that better addresses the concerns of winegrowers. The results provide the local winegrowers with information necessary to understand current processes, as well as historical and future viticulture trends at the scale of their site, thereby facilitating decisions about future response strategies.

El archipiélago Canario, conocido en el pasado como las Islas del Vino, fue una gran potencia en la elaboración y comercialización del vino, sobre todo de caldos elaborados con la variedad Malvasía.