Limiting magnesium availability: a novel approach to managing brettanomyces spoilage in winemaking

In temperate climates, such as in the North of Spain, the use of irrigation in the vineyard has not been required, due to the usual rainfall from June to August. In some large vineyards, irrigation management has been carried out, based on occasional support irrigation, or for the application of nutrients (fertigation). Currently it is necessary to implement decision support models to manage irrigation water in real time and avoid misuse of a scarce resource. Moreover, quality standards must be achieved, as in the previous rainfed viticulture.

Volatile phenols from wildfire smoke are absorbed by wine grapes, resulting in undesirable smoky and ashy sensory attributes in the affected wine.[1] Unfortunately the severity of wildfires is increasing, particularly when grapes are ripening on the vine. The unwanted flavors of the wine prompted a need for solutions to prevent the uptake of smoke compounds into wine grapes. Films using cellulose nanofibers as the coating forming matrix were developed as an innovative means to prevent smoke phenols from entering Pinot noir grapes. Different film formulations were tested by incorporating low methoxy pectin or chitosan.

Melatonin is a bioactive compound present in foods and beverages such as wines. During alcoholic fermentation, yeast transforms tryptophan into certain indole compounds, including melatonin. This paper aims to develop and validate a free solvent analytical method by ultra-high performance liquid chromatography coupled with high resolution mass spectrometry (UHPLC/MS-MS) to determine melatonin and its precursors (L-tryptophan, tryptamine, serotonin, tryptophol, N-acetylserotonin, 5-hydroxytryptophan, and 3- indoleacetic) that appropriately prevent the matrix effect.

Grape quality potential for wine production is strongly influenced by environmental parameters and agronomic factors. Several studies underline the rootstock effect on scions vegetative growth and berry composition [1] with an impact on wine quality. Rootstocks are promising agronomic tools for climate change adaptation and in most grape-growing regions the potential diversity of rootstocks is not fully used and only a few genotypes are planted. Moreover, little is known about the effect of rootstock genetic variability on the aromatic composition in wines.

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).