Identification of key-odorants in Sauternes Wines

Enhancing the mineral wine profile: from authentication to identification by artificial intelligence for enhanced security. Analysis of a wine’s mineral concentration profile provides a distinctive fingerprint for each cuvée. Unlike organic profiles, this identification signature remains stable over time and can be deciphered using direct analysis by inductively coupled mass spectrometry (icp-ms).

Late spring frost is a major challenge for various winegrowing regions across the world, its occurrence often leading to important yield losses and/or plant failure. Despite a significant increase in minimum temperatures worldwide, the spatial and temporal evolution of spring frost risk under a warmer climate remains largely uncertain. Recent projections of spring frost risk for viticulture in Europe throughout the 21st century show that its evolution strongly depends on the model approach used to simulate budburst. Furthermore, the frost damage modelling methods used in these projections are usually not assessed through comparison to field observations and/or frost damage reports.
The present study aims at comparing frost risk projections simulated using six spring frost models based on two approaches: a) models considering a fixed damage threshold after the predicted budburst date (e.g BRIN, Smoothed-Utah, Growing Degree Days, Fenovitis) and b) models considering a dynamic frost sensitivity threshold based on the predicted grapevine winter/spring dehardening process (e.g. Ferguson model). The capability of each model to simulate an actual frost event for the Vitis vinifera cv. Chadonnay B was previously assessed by comparing simulated cold thermal stress to reports of events with frost damage in Chablis, the northernmost winegrowing region of Burgundy. Models exhibited scores of κ > 0.65 when reproducing the frost/non-frost damage years and an accuracy ranging from 0.82 to 0.90.
Spring frost risk projections throughout the 21st century were performed for all winegrowing subregions of Bourgogne-Franche-Comté under two CMIP5 concentration pathways (4.5 and 8.5) using statistically downscaled 8×8 km daily air temperature and humidity of 13 climate models. Contrasting results with region-specific spring frost risk trends were observed. Three out of five models show a decrease in the frequency of frost years across the whole study area while the other two show an increase that is more or less pronounced depending on winegrowing subregion. Our findings indicate that the lack of accuracy in grapevine budburst and dehardening models makes climate projections of spring frost risk highly uncertain for grapevine cultivation regions.

The dynamic sequence of physiological events along the three-months of berry development from anthesis to ripe stage has been thoroughly investigated. Most studies were performed on average samples, taking care to crush enough fruits to fairly represent the overall trend of the future harvest. However, phenological stages like 30% caps off (EL25) highlights the asynchronous nature of this population. Consequently, softening, onset of sugar accumulation and coloration were melted by asynchrony in a developmental mumbo jumbo, until their respective timing could be clarified by single berries approaches.

It is quite common nowadays to carry out analyses which allow to control the ageing of spirits that are aged in wood casks. Many control parameters have been previously studied, such as the concentration of different phenolic compounds or the Total Polyphenol Index, in order to better understand the ageing process of wood aged spirits. On the other hand, it is frequent to analyse as a physical parameter the colour of those spirit samples, by stating them as an array of three coordinates from various colour spaces as CIE L*a*b* or CIE L*C*H*.

Multiple Factor Analysis (MFA) According to the Chenin blanc Association of South Africa, there are three recognized dry wine styles, Fresh and Fruity (FF), Rich and Ripe Unwooded (RRU), and Rich and Ripe Wooded (RRW), classically attributed with the help of sensory evaluation. One of the “rapid methods” has drawn our attention for the purpose of simplifying and making style attribution for large sample sets, evaluated during different sessions, more robust. Polarized Projective Mapping (PPM) is a hybrid of Projective Mapping (PM) and Polarised Sensory Positioning (PSP). It is a reference-based method in which poles
(references) are used for the evaluation of similarities and dissimilarities between samples.