Modeling viticultural landscapes: a GIS analysis of the viticultural potential in the Rogue Valley of Oregon

In response to the growing demand for rapid, precise, and efficient methods of quantifying volatile compounds in alcoholic beverages, this study presents a novel approach for the determination of higher alcohols in wine, port wine, and brandy.

Aim of the study was to investigate the metabolomic differences between Chilean Cabernet Sauvignon wines, divided according to their quality in two main groups: “Varietal” and “Premium”, and to point out metabolites tentative markers of their chemical signature and sensorial quality. Initially, 150 (50 x 3 biological replicates) experimental wines were produced by the same semi-industrial process, which covered 8 different Chilean valleys. The wine classification made by experts, divided the wines into two major groups (“Varietal” and “Premium”) and four subgroups (two for each major group). All the samples were analyzed according to a robust LC-MS based untargeted work-flow (Arapitsas et al 2018), and the proposed minimum reporting standards for chemical analysis of the Metabolomics Standards Initiative (Sumner et al 2007)

The main objective of this paper is to establish considerable differences between wines from three wine areas or terroir, made with cv Torrontes Riojano.

Sauvignon blanc is characterized by distinctive aromas, both fruity and herbaceous. The “green” character has been attributed to the methoxypyrazines, while the “fruity” character is associated with polyfunctional mercaptans . Polyfunctional mercaptans are of great significance due to their high impact on wines and associated low perception thresholds.
Elemental sulfur (S⁰) is widely used to protect grapevines from powdery mildew.

It is fundamental for wineries to know the potential yield of their vineyards as soon as possible for future planning of winery logistics. As such, non-invasive image-based methods are being investigated for early yield prediction. Many of these techniques have limitations that make it difficult to implement for practical use commercially. The aim of this study was to assess whether yield can be estimated using images taken in-field with a smartphone at different phenological stages.