Physico-chemical properties of vine pruning residues with potential as enological additive

Trichoderma is one of the most widely used fungal biocontrol agents on vineyards due to its multiple benefits on this crop, such as its fungicidal and growth promoting capacity. In this work, we have analyzed the effect on the concentration of nutrients in grapevine leaves and on the quality of the grape must after spraying an autochthonous strain of Trichoderma harzianum and one of the main secondary metabolites produced by this genus, 6-pentyl-α-pyrone (6PP).

The recovery of ancestral or minority vine varieties has been gaining great interest in recent years, among other reasons because it is likely that some of these varieties, due to the fact that they are found in relict areas, have a greater potential for adaptation to external factors (biotic or abiotic) and can minimize the effects that climate change is causing in viticulture. Varieties that can be grown at altitude are currently being sought to combat rising temperatures and prolonged extreme drought conditions. In Catalonia, the Pyrenean expansion of vineyard cultivation is documented from the 10th century and has been related to the “small climatic optimum” (9th-12th centuries) and also to seigniorial power.[1] But different adverse climatic periods and the arrival of Phylloxera by the late 19th century made many of these crops disappear.[2]

Condensed grape tannins play a major role in the organoleptic properties and quality of red wine. Recently, a new sub-family of macrocyclic condensed tannins has been identified in red wine and named “crown tannins”. Indeed, the first compound of the family identified and characterised by NMR was the crown procyanidin tetramer which is composed of a macrocyclic structure composed of four (-)-epicatechins link together by B-type interflavanoid linkage in the following an alternative sequences of C4-C8 and C4-C6 linkage. The 3D structure of this unusual crown procyanidin family reveals a central cavity in the molecule [1].

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).

The characterization of chemical compounds related with quality of grape must and wine is relevant for the viticulture and enology fields. Analytical methods used for these analyses require expensive instrumentation as well as a long sample preparation processes and the use of chemical solvents. On the other hand, near-infrared (NIR) spectroscopy technique is a simple, fast and non-destructive method for the detection of chemical composition showing a fingerprint of the sample. It has been reported the potential of NIR spectroscopy to measure some enological parameters such as alcohol content, pH, organic acids, glycerol, reducing sugars and phenolic compounds.