Freeze-thaw temperature oscillations promote increased differential gene expression during grapevine bud dormancy

The postharvest dehydration of grapes is a traditional practice to obtain wines with unique traits (e.g., sweet, dry/reinforced).

Vineyard canopy features such canopy porosity and fruit exposure influenced microclimate, fungal disease incidence and grape composition. An objective, rapid and non-invasive method to assess and map the canopy status is needed to apply in precision viticulture. A new method for canopy status assessment and mapping based on non-invasive machine vision was applied in commercial vineyards in this work.

Although some wine production processes still rely on post-production evaluation and off-site laboratory analysis, the new winemaking industry is aware of a need for a better knowledge of the process to improve the properties of the final product. Thus, more and more wineries are interested in incorporating quality-by-design (QbD) strategies instead of postproduction testing because of the possibility to early detect deviations in fermentation or any other wine process. This would allow to detect unwanted situations and eventually to ‘readjust’ the process, thus minimizing rejects.

Soil is a reservoir of microorganisms playing important roles in biogeochemical cycles and interacting with plants whether in the rhizosphere or in the root endosphere. Through rhizodeposition, plants regulate their associated microbiome composition depending on the environment and plant factors, including genotypes. Since the phylloxera crisis, Vitis vinifera cultivars are mainly grafted onto American Vitis hybrids. Rootstocks play a pivotal role in the grapevine development, as the interface between the scion and the soil.

The main goal of the current study is the development of a cost-effective and easy-to-use method suitable for use in the laboratory of commercial wineries to analyze wine aroma. Additionally, this study attempted to establish a prediction model for wine quality gradings based on their aroma, which could reveal the important aroma compounds that correlate well with different grades of perceived quality METHODS: Parameters of the SHS−GC−IMS instrument were first optimized to acquire the most desirable chromatographic resolution and signal intensities. Method stability was then exhibited by repeatability and reproducibility. Subsequently, compound identification was conducted. After method development, a total of 143 end-ferment wine samples of three different quality gradings from vintage 2020 were analyzed with the SHS−GC−IMS instrument. Six machine learning methods were employed to process the results and construct a quality prediction model. Techniques that aim to explain the model to extract useful insights were also applied.