“Terroir” and climate change in Franconia / Germany

During wine aging, small amounts of oxygen are absorbed and initiate a cascade of oxidation reactions. These aging reactions create many products including ethylidene-bridged oligomers and polymers of endogenous polyphenols, like flavan-3ols.

Recognizing the influence of alcohol preference on health behaviors is essential for developing tailored interventions that effectively promote healthier lifestyles and optimize disease management strategies in the vulnerable population of patients with cardiometabolic diseases (CMD). The present study aims to provide valuable insights into how alcohol preference relates to dietary habits and medication adherence among patients with CMD diseases.

A.O.C. huile d’olive de Nyons et olives noires de Nyons

Controlling the development of the bacterial population during the winemaking process is essential for obtaining correct wines[1]. Carbonic Maceration (CM) wines are recognised as high-quality young wines. However, due to its particularities, CM winemaking implies a higher risk of bacterial growth: lower SO2 levels, enrichment of the must in nutrients, oxygen trapped between the clusters… Therefore, wines produced by CM have slightly higher volatile acidity values than those produced by the destemming/crushing method[2].

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.