Fractal analysis of the hydrological information obtained from high-spatial resolution dems: application in terroir zoning of d.o. campo de Borja (Spain)

Among the factors that influence the sensory quality, style, safety, sustainability, and sense of place of a wine, the contributions of microbial biodiversity are widely becoming more recognized. Throughout winemaking, multiple biochemical reactions are performed by a myriad of different microorganisms interacting in many ways.

Context and purpose of the study. Intra-varietal variability for key physiological and oenologically important traits can be exploit in viticulture following the consistently higher environmental pressure driven by climate change.

Certain yeast species belonging to the Pichia genus are known to form a distinctive film on grape must and wine. In a mixed-culture type fermentation, Pichia spp. (P. kluyveri in particular) are known to impart beneficial oenological attributes. In this study, we report on an easy isolation method of Pichia spp. from grape must by exploiting their film-forming capacity on media containing 10% ethanol. We isolated and identified two Pichia species, namely Pichia kudriavzevii and Pichia kluyveri, and subsequently co-inoculated them with Saccharomyces cerevisiae to ferment Gewürztraminer musts. Noteworthy differences included a significant increase in the 2-phenethyl acetate levels with the P. kluyveri co-fermentation and a general increase in ethyl esters with the P. kudriavzevii co-fermentation. Both Pichia co-inoculations yielded higher levels of glycerol in the final wines. Based on all the wine parameters we tested, the P. kluyveri strain that was isolated performed similarly to a commercial P. kluyveri strain.

Nuclear magnetic resonance spectroscopy (NMR) is a high-tech analytical method that recently found its way into the field of wine analysis with special focus on wine authentication.

Modern grapevine breeding programs have overcome many challenges using genomic selection, which has allowed breeders to make targeted selections at earlier stages in the breeding process. However, the cost of genetic testing may present a burden for some programs, and markers often struggle to accurately predict quantitative traits. Recent advances in high throughput, high-dimensional data have provoked investigation into the use of high-dimensional phenomics as a low-cost addition to the grape breeder’s toolkit that may offer advantages in predicting quantitative traits. High-dimensional secondary trait (HDST) data has been employed in annual crops for prediction of agriculturally important traits such as yield.