Analysis of phenolic compounds typically involve spectrophotometric methods as well as liquid chromatography combined with DAD, fluorimetric, or MS detection. However, the complexity of wine phenolic composition generated, in recent years, attention towards other analytical approaches, including those allowing rapid and inexpensive operations. Voltametric AIM Oxidation of white wine phenolics occurs at different stages during winemaking and storage and can have important implications for wine sensory quality. Phenolic compounds, in particular those with a ortho-diphenol moiety, are main target of oxidation in wine. Strategies for the methods are particularly suited for the analysis of oxidizable compounds such as phenolics. The redox-active species can be oxidized and reduced at the electrode, therefore, applications of electrochemistry have been developed both to quantify such species, and to probe wine maturation processes.3 The project on the diversity of Italian wines aims at collecting and analysing large-scale compositional dataset related to Italian white wines.

A seven years survey (2003 to 2009) has been carried out over old traditional vineyards cv. Cencibel in La Mancha region (Spain). Seven plots with more than 35 years old were sampled from veraison to harvest, measuring soluble solids (ºBaumé) and acid concentration (g/l in tartaric acid).

The overall quality of aged wines is in part due to the development of complex aromas over a long period (1.) The apparition of this aromatic complexity depends on multiple chemical reactions that include the liberation of odorous compounds from non-odorous precursors. One example of this phenomenon is found in dimethyl sulphide (DMS) which, with its characteristic odor truffle, is a known contributor to the bouquet of premium aged wine bouquet (1). DMS supposedly accumulates during the ten first years of ageing thanks to the hydrolysis of its precursor dimethylsulfoniopropionate (DMSp.) DMSp is a possible secondary by-product from the degradation of S-methylmethionine (SMM), an amino acid iden- tified in grapes (2), which can be metabolized by yeast during alcoholic fermentation.

The gradual change in rainfall patterns experienced in the south of France vineyards, especially around the Mediterranean sea, means that the vines are increasingly subject to summer drought. The winegrowers developped the use of irrigation techniques to ensure the maintenance of competitive yields in the production of wines under Protected Geographical Indication label. In practice, drip irrigation pipes can be installed above the ground or buried into the soil as well as at different distances from the vine row. The objective of this study was to examine the profiles of the wet bulbs of the soil obtained from two drip irrigation systems : aerial drip located under the vine row and subsurface drip placed in the middle of the inter-row. This experiment took place over two consecutive seasons (2020-2021) on a 3.4 ha Viognier plot in the Mediterranean region (PGI Oc, France) on sandy clay soil. The annual rainfalls were less than 400 mm. Soil water content probes were installed at different depths (20 – 40 – 60 – 80 cm) and at different lateralities from the vine row (30 – 60 – 90 – 120 cm) to control the formation of the soil wet bulb during irrigation. The mapping and the analysis of the data allowed a better understanding and differentiation of the water percolation when irrigating with subsurface or aerial drip. For the same amount of water and without differences of vine water status, it is shown that in a subsurface drip irrigation situation, the size of the wet bulb formed is larger than in aerial drip irrigation system.

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.