A new chemiluminescence method related to molecules derived from Botrytis cinerea for characterization of Aszu wines from Tokaj, from Hungary

Implementing artificial intelligence (AI) in viticulture and enology is a rapidly growing field of research with an essential number of potential practical applications.

EU countries are in the top 16 of the world’s wine producers. To respond to a public health concern, caused by SO2 excessive exposure

Vers la fin du XX siècle, la vitiviniculture argentine a subi une profonde transformation qualitative atteignant toute la filière. L’analyse de l’évolution de la superficie des vignobles, l’élaboration des vins, la consommation et les exportations, permet de mettre en évidence ces changements. Dans ce contexte, l’origine apparaît comme un outil de force, d’expansion et de succès sur les marchés.

The importance of polyphenols, which are present in many vegetables and grapes too, is well-know and documented. Specific research works about the red grape

Barolo DOCG is an economically important wine producing region in Northwest Italy. It is a small region of approximately 70 km2 gross area. The topography is very complex with steep sloped hills ranging in elevation from below 200 m to 550 m. Barolo DOCG wine is made exclusively from the Nebbiolo grape. Bioclimatic indexes are often used in viticulture to gain a better understanding of broader climate trends which can be compared temporally and geographically. These indexes are also used for identifying potential phenological timing, growing region suitability, and potential risks associated with expected climatic changes. Understanding how topography influences bioclimatic indexes can help with understanding of mesoscale climate behaviour leading to improved decision making and risk management strategies. The average monthly maximum and minimum temperatures, the Cool Night Index, the Huglin Index, and the monthly diurnal range (from July to October) were calculated using data from 45 weather stations within a 40 km radius of the Barolo DOCG growing area between the years 1996 and 2019. Linear and multiple regression models were developed using independent variables (elevation, aspect, slope) extracted from a digital elevation model to identify significant relationships. Bioclimatic indexes were then kriged with external drift using independent variables that showed significant relationships with the bioclimatic index using a 100 m resolution grid. The maximum monthly temperatures and the Huglin Index showed consistent significant negative relationships with elevation in all years. The minimum monthly temperatures showed no relationship with elevation but in some months a small but significant relationship was observed with aspect. Due to the lack of a relationship between minimum monthly temperatures and elevation compared to the significant relationship between maximum monthly temperatures and elevation, monthly diurnal range had a negative relationship with elevation.