Use of a recombinant protein (Harpin αβ) as a tool to improve phenolic composition in wines

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The occurrence of faults and taints in wine, such as those caused by microbial spoilage or various taints, have resulted in significant financial losses to wine producers. The wine industry commits significant financial resources towards fining and taint removal processes each year. Fining involves the addition of one or more adsorptive substrates to juice or wine to bind certain components, thus reducing their concentration [1]. However, these processes are often not selective and can also remove desirable flavour and aroma compounds.

Using grapevine genetic diversity is one of the strategies to adapt viticulture to climate change. In this sense, assessing the plasticity of cultivars in their responses to environmental conditions is essential. For this purpose, the drought tolerance of Grenache, Tempranillo and Semillon cultivars grafted onto SO4 was evaluated at two experimental vineyards, one located in Valencia (Spain) and the other in Bordeaux (France). This was done by assessing gas exchange parameters, water relations and leaf hydraulic traits at the end of the season.

Vineyards are complex agro-ecosystems with high spatial and temporal variability. An efficient training system may counteract the adverse effects of this variability. Moreover, considering the climate change issues, choosing an efficient training system that enhances water use and protects the vines from radiative thermal stress has become a priority for the farmers. A multidisciplinary approach that assesses the soil-crop-yield-wine relationships of vineyards in a distributed and holistic way could bring added knowledge on the behavior of the different training systems. This ongoing research aimed to implement a multidisciplinary approach to study the behavior of “Aglianico del Vulture” grapevines trained with two different systems: a spurred cordon (SC) and an “Alberello in parete” (AL), grown in a high-quality wine production area of Basilicata region (Italy). The approach merged several methods and scales of soil, ecophysiology, must/wine quality, and spectral data collection to assess the influence of the training system. Homogeneous zones (HZs) in both training systems were defined through a procedure based on geomorphological classification, unmanned aerial vehicles (UAV) images analysis, and a traditional soil survey supported by geophysical scanning. During the 2021 season, TDR probes monitored soil water content, while grapevine health status was assessed using eco-physiological measurements (LWP, chlorophyll content, PSII photosynthetic efficiency, LAI, and point-based field spectroscopy). These grapevine in-vivo measurements validated the spectral vegetation indexes (NDVI, RENDVI, CVI, and TVI) derived from the UAV multispectral imagery, which monitored the grapevine status in a distributed and non-invasive way. Grape yield, quality of berries, must and wine were measured to assess the effects of the training systems. The first experimental year results showed the variability of the vineyards and revealed relationships among soil parameters, crop characteristics, and vegetation indices of the SC and AL training systems. This multidisciplinary study could bring new insights into the vineyard training system’s effects on grape yield and wine quality.

The Matrix Assisted Laser Desorption/Ionization–Time-Of-Flight Mass Spectrometry (MALDI-TOF MS) technology is commonly used in food and medical sector to identify yeast or bacteria species isolated from a nutritive culture media. Since a decade, brewery and oenology industries have been attracted to this method which combines fast analysis times, reliability and low cost of analysis. Briefly, this method is based on the comparison of the MALDI-TOF/MS protein spectra of an isolated colony of yeast or bacteria with those contain in a manufacturer’s reference protein spectra database. Initiated in 2015, the creation of the first oenological mass spectra database has proved to be essential for increase quality of species identification.