Effect of abiotic stress and grape variety on amino acid and polyamine composition of red grape berries

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 ’Furmint’ is the most important grape variety in the Tokaj Wine Region, constituting around 65% of its vineyard area. Before the phylloxera disease many types were grown, but as selection started in the 20th century, its diversity dramatically narrowed. As a result, the cultivation of Furmint was based mainly on two heavy-cropping clones, T.85 and T.92 at the end of the ’80s. Aims of present clone research take into account that after solely quantity as target, quality emerged in the 1990’s and most recently, typicity appeared as more private estates began their own selection program.

Targeted analytical methods can overlook compounds that are a priori unknown to play a role in the mouthfeel sensations. This limitation can be overcome with the information provided by untargeted metabolomic analysis using UPLC‐QTOF-MS. To this end, an untargeted metabolomic approach applied to 42 red wines has allowed development of a model with predictive capacity by cross-validation for the “dry”, “oily” and “unctuous” sensations perceived by a sensory panel. The optimal PLS model for “dry” retained compounds with positive regression coefficients (≥ 0.17) including a trimer procyanidin, a peptide, and four anthocyanins.

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.

The yeast ecosystem in grape musts is quite broad and depends on the region and the health of the grapes. Within this, there are yeasts that can generate fermentative deviations and/or cause defects in the wine. It is very important to address this issue because there are significant economic losses in the wine industry when the fermentation process and/or the organoleptic characteristics of the wine are negatively affected, even more today since climate change has a marked effect on the composition of this ecosystem. The aim of this work is to characterize the behavior regarding detrimental oenological features of potential spoilage yeasts isolated from viticultural environments.