Influence of different Lachancea thermotolerans strains in wine acidity

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.

Grapevines have traditionally been grown in semi-arid areas, but viticulture is now compromised by climate change. Therefore, it is necessary to implement environmentally friendly viticulture practices to adapt grapevines to current climatic conditions. In this context, organic mulches offer many benefits, such as reduced soil erosion and increased organic matter, soil water content and crop productivity. However, these practices must not compromise grape and wine quality. Therefore, the objective of this study was to evaluate the effect on wine physicochemical and phenolic composition and sensorial properties of different soil management practices on the vine row. Over four years, five soil treatments were examined in two different vineyards.

Ultraviolet radiation (UVR) is a minor part of the solar spectrum, but it represents an important ecological factor that influences many biological processes related to plant growth and development. In recent years, the application of UVR in agriculture and food production is emerging as a clean and environmentally friendly technology.
In grapevine, many studies have been conducted on the effects of ambient levels of UVR, but there are few considering the effects of UV-B application on grape phenolic composition under commercial growing or postharvest conditions.

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).

Within the DOCa Rioja three main production areas are differentiated: Rioja Alta (RA), Rioja Alavesa (RAv) and Rioja Oriental (RO). They are three diverse territories with particular characteristics that are claimed to give rise to differentiated profiles. The present work aims at evaluating the sensory diversity of young commercial red wines in these three subregions. Therefore 30 young red wines (mainly Tempranillo and vintage 2021), ten from each subregion, were sensory described following a non-verbal free sorting task and a verbal free comment task by 32 well-established Rioja winemakers.