Study of the sensory dimension of the wine typicality related to a terroir and crossing with their viticultural and oenological characteristics

Must clarification is an important step occurring just after grape extraction in the elaboration of white wine, consisting in a solid-liquid separation. Traditionally, low must turbidity, around 50-150 NTU, is generally reached in white winemaking in order to prevent reductive aromas and facilitating alcoholic fermentation. Alternatively, a higher turbidity (300 NTU or above) can be sought for reasons such as a better expression of grapes identity (terroir), or for getting a must matrix that could supposedly lead to wines having greater ageing potential.

Brettanomyces yeast can negatively impact the quality and stability of wines, posing a significant challenge to winemakers. [1] This study aims to develop novel management practices to limit Brettanomyces impact on wines by evaluating the effectiveness of electrodialysis (ED) technology in removing magnesium (Mg2+) from wine to prevent the development of Brettanomyces yeast. The ED technique utilizes charged membranes to extract ions from the wine, and it is considered an alternative to cold stabilization that requires less energy. [2]

The work aims to characterise the colour features of the wines of two new resistant varieties breeeded at the Edmund Mach Foundation (Italy) and recently inscribed in the Italian National Registriy of Vine Varieties.

Global changes in temperature patterns necessitate the development of viticultural adaptation strategies. One promising approach involves modifying the training system and elevating trunk height. This study explored the potential of raising the vine trunk as an adaptive strategy to counteract the effects of increasing temperatures and delay ripening. Thermal conditions, radiation levels, and must composition were measured at different heights (10 and 150 cm) in a commercial vineyard of the minority variety Maturana Blanca, trained on a vertical cordon.

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).