Predicting provenance and grapevine cultivar implementing machine learning on vineyard soil microbiome data: implications in grapevine breeding

Multiple sclerosis (MS) is a multifactorial autoimmune disease associating demyelination and axonal degeneration developing in young adults and affecting 2–3 million people worldwide. Plant polyphenols endowed with many therapeutic benefits associated with anti-inflammatory and antioxidant properties represent highly interesting new potential therapeutic strategies. We recently showed the safety and high efficiency of grape seed extract (GSE), a complex mixture of polyphenolics compounds comprising notably flavonoids and proanthocyanidins, in an experimental autoimmune encephalomyelitis (EAE) mouse model of MS.

Soil is important in the carbon cycle and the dynamics of greenhouse gases (CO2, CH4 and N2O). Key soil characteristics, such as organic matter content, texture, structure, pH and microbial activity, play a determining role in GHG emissions[1]. The objective of the study is to delimit different types of soil, with different soil management and to be able to verify the differences in CO2, CH4 and N2O emissions. The study was carried out in a vineyard of Bodegas Campo Viejo in Logroño (La Rioja), whose plant material is Vitis vinifera L. cv. Tempranillo.

The increased intensity and frequency of heatwaves, coupled with prolonged periods of drought, are a significant threat to viticulture worldwide. During these conditions the more exposed leaves can show visible symptoms of heat damage. We monitored the functionality of photosystem II (PSII) in the field to better understand the impact of heatwaves on canopy performance. A factorial experiment was established in summer 2023 using Shiraz grapevines in the Barossa valley of South Australia, involving water-stressed and well-watered vines.

Despite the numerous research studies carried out in recent years, the study of wine aroma remains of great interest due to its complexity. Wine maturation in oak barrels is described as an important step in the production of quality wines. In fact, oak wood develops several aromatic nuances through its toasting which can be released into the wine. A great deal of work has been performed in order to identify the wood-derived volatile compounds that contribute to wine aroma (e.g., whisky-lactone, maltol, eugenol, guaiacol, vanillin).

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).