Overall conceptual characterization of aged dry white wines using a mental descriptive questionnaire

The use of grapevine genetic diversity is a way to mitigate the negative impacts of climate change on viticulture systems. Leaf epidermal flavonoids (including flavonols and anthocyanins) are involved in plant defense mechanisms against environmental stresses, like high temperatures or excessive solar radiation [1,2]. Among other factors, they modulate light absorption, which reduces photoinhibition processes in photosynthetic tissues [1]. Therefore, the identification of grapevine cultivars with an increased content on leaf epidermal flavonoids arises as a potential avenue to improve grapevine tolerance to some detrimental environmental stresses.

Botrytis cinerea has more than 1200 host plants and is one of the most important plant pathogens in viticulture. Under certain environmental conditions, it can lead to the development of a noble rot, which results in a specific metabolic profile, altering physical texture and chemical composition. The other microbes involved in this process and their functional genes are poorly characterised. We have generated metatranscriptomic [1,2] and DNA metabarcoding data from three months of the Furmint grape variety, representing the four phases of noble rot, from healthy berries to completely dried berries.

This essay was aimed to describe the clonal diversity of Oenococcus oeni in the malolactic fermentation of the carbonic maceration (CM) winemaking. The free and the pressed liquids from CM were sampled and compared to the wine from a standard winemaking with previous destemming and crushing (DC) of grapes [1]. O. oeni strain typification was performed by PFGE as González-Arenzana et al. described (2014) [2]. Results showed that 13 genotypes, referred as to letters, were distinguished from the 49 isolated strains, meaning the genotype “a” the 27%, the “b” the 14%, the “c” the 12%, the “d and e” the 10 % each other, and the remaining ones less than the 8% each one.

Two of the most harmful microbial metabolites for human health that can be present in wines and either fermented or raw foods are biogenic amines (BA) and ochratoxine A (OTA). Winemakers are aware of the need to avoid their presence in wine by using different strategies, one of them is the use of enzymes. Some recombinant laccases have been characterized and revealed as potential tools to degrade these toxic compounds in wine[1], specifically biogenic amines[2].

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).