Untargeted metabolomics to identify potential chemical markers responsible for the permissiveness of red wines against Brettanomyces bruxellensis

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.

Over the past two years, the AWRI has received 69 wine samples suspected of being affected by mousy off-flavour. The character has been mostly observed in white wines.

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.

L’Albana è il vitigno a bacca bianca tradizionale delle colline della Romagna, dove é presente per più di 2.500 ha. Con le sue uve si produce il vino “Albana di Romagna”, una delle più storiche D.O.C.G. italiane essendo stata costituita nel 1987. La maggiore concentrazione di vigneti di Albana si trova nell’Imolese e nelle colline del Ravennate, ma ben conosciuta per la qualità del prodotto é anche la produzione di Bertinoro, nel Forlivese.

Stilbenes, a kind of non-flavonoid phenolic compounds, have been reported to be responsible for various beneficial effects. Their biological properties include antibacterial and antifungal effects, as well as cardioprotective, neuroprotective and anticancer actions (Guerrero et al. 2009).Several strategies can be used to increase stilbene content in grapes and one of them could be the use of elicitors such as methyl jasmonate. The use of this elicitor has been proven to be efficient in the production of secondary metabolites which increases the quality of wines, but its use also has some drawbacks such as its low water solubility, high volatility, and its expensive cost (Gil-Muñoz et al. 2021).