Mining microbiome data to identify antagonists of grapevine downy mildew (Plasmopara viticola)

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.

Amarone is an Italian red wine produced in the Valpolicella area, in north-eastern Italy. Due to its elaboration with withered grapes

Grapevine (Vitis Vinifera L.), one of the most important cultivated fruit crops, is facing significant challenges due to climate change. Specifically, increasing temperatures negatively impact the physiological traits and disrupt plant phenology. Additionally, increased virulence in pathogen attacks and pests leads to significant yield loss, requiring widespread application of plant protection products. Traditional agronomic practices offer only partial mitigation, requiring the development of precise and effective intervention strategies. The economic worth of viticulture has prompted continuous efforts in grapevine genetic improvement programs, traditionally involving conventional breeding and clonal selection that, however, are complex and time-consuming approaches.

To face emerging competition and challenges, wine producers globally rely on precision viticulture (PV) solutions to boost productivity, enhance quality, increase profitability, and reduce the environmental impact of vineyards. Current pv methods predominantly use multispectral sensor data from several platforms (satellites or vineyard installations). However, these applications generally use data analysis strategies lacking physiological grapevine support.

In the context of global change, the environmental conditions are expected to be more stressful for viticulture. The choice of the rootstock may play a crucial role to improve the adaptation of viticulture to new biotic and abiotic threats (Ollat et al., 2016). However, the selection of interesting traits in rootstock breeding programs is complex because of the combination of multiple targets in a same ideotype. In this sense, the integration of studies about the genetic architecture for desired biotic and abiotic response traits allow us to identify genomic regions to combine and those with interesting pleiotropic effects.