Analysis of volatile composition of interaction between the pathogen E. necator and two grapevine varieties

Grapevine resilience to climate change has become one of the most pressing topics in the Viticulture & Enology field. Vineyard health demands understanding the mechanisms that explain the direct and indirect interactions between environmental stressors. The current climate change scenario, where drought and heat-wave are more frequent and intense, strongly demands improving our knowledge of environmental stresses. During a heatwave, the ambient temperature rises above the plant’s average tolerance threshold and, generally, above 35 oC plant’s adaptation to heat stress is activated.

Atmospheric CO2 levels have been rising continuously since the industrial revolution, affecting crop physiology, yield and quality of harvest products, and grapevine is no exception [1]. Most of previously reported studies used potted plants in controlled environments, and explored grapevine response to relatively high CO2 levels, 700 ppm or more. The vineyardFACE, established in Geisenheim in 2012, uses a free air carbon dioxide enrichment (FACE) system to simulate a moderate (ambient +20%) increase in atmospheric CO2 in a vineyard planted with cvs. Cabernet-Sauvignon and Riesling grafted on rootstock 161-49 Couderc and SO4, respectively.

Pomace is one of the main residues generated by the wine industry and represents an environmental problem. Currently, there is a growing interest in the revaluation of these products because different bioactive compounds can be obtained from them, such as polyphenols, grape seed oils and polysaccharides. Red grape pomace can be an important source of polysaccharides, but they are currently little studied and even less with viable and environmental extraction processes (green extraction), such as flash extraction. The residual amount of the fraction rich in pectin (residual pulp) and component rich in hemicellulose in the pomace and the strength of association of the pectin with the cellulose-xyloglucan network depend on the degree of extractability of the polysaccharides in red winemaking and on the winemaking 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).

[lwp_divi_breadcrumbs home_text="IVES" use_before_icon="on" before_icon="||divi||400" module_id="publication-ariane" _builder_version="4.20.4" _module_preset="default" module_text_align="center" module_font_size="16px" text_orientation="center"...