Applicability of spectrofluorometry and voltammetry in combination with machine learning approaches for authentication of DOCa Rioja Tempranillo wines

In winemaking grapes, it is known that most aroma compounds are present as non-volatile precursors, such as glycosidic precursors. In fact, there is strong evidence supporting the connection between the content of aroma precursors and the aromatic quality of wine [1]. Acid hydrolysis is preferred to reveal the aroma potential of winemaking grapes, as it predicts more accurately the chemical rearrangements occurring during fermentation in acidic environments [2]. In this study, a method involving a fast fermentation followed by acid hydrolysis at 75ºC was used to evaluate the accumulation of aroma compounds over time in fractions obtained from six different varieties of winemaking grapes.

In a recent context where consumers pay an increasing attention to sustainability and eco-friendly aspects in the decision-making process, the use of the resistant varieties in the wine sector have returned to the attention. In this context, the use of mould-resistant grape varieties would be an opportunity for sparkling wine producers as it can reduced the pesticide utilization in grape management and hence production costs.
However, the use of the resistant varieties to produce the base wine may be strongly influenced due to its requirements for a particular balance between sugars and acidity to ensure the quality of the final product. In addition, the aromatic profile of base wine plays a crucial role in the perception of the quality of the sparkling wine.

By dissecting the root system architecture (RSA) into its underpinning components (e.g. root emission, axial growth, radial growth, branching, root direction or tropism) and identifying the relationships between them, functional-structural 3D root models are promising tools for analyzing the diversity and complexity of root system phenotypes with Genotype × Environment interactions. The model parameters are assumed to be synthetic traits, less influenced by the environment, and consequently with less polygenic architectures than the integrative RSA traits they drive. Root models can serve as a basis for in silico development of root system ideotypes by highlighting the developmental processes and parameters that most likely influence RSA fitness.

The interest in sustainable and ecologic agricultural practices in grapevine has grown significantly in recent years in the context of ecological transition. Organic mulches are treatments that support the circular economy and positively affect the soil and the plant. They are an alternative to herbicides and other conventional practices since they may influence soil moisture, erosion, structure and weed control. However, their effects on the soil and must microbiota remain unknown.

Grapes are one of the world’s primary fruit crops, and pruning activities generate high amounts of annual wood wastes [1]. These pruning shoots contain valuable phenolic compounds and could have numerous potential applications [1,2]. Consequently, the aim of this work was to evaluate the physico-chemical properties of vine pruning residues with potential as enological additives. For this purpose, grapevine shoots from 12 varieties grown in Chile were collected during the winter of 2021.