applicazione dei metodi isotopici e dell’analisi sensoriale negli studi sull’origine dei vini

A novel and efficient Dispersive Liquid-Liquid Microextraction (DLLME) method coupled with gas chromatography–mass spectrometry (GC–MS) was developed to determine 33 key aroma compounds (esters, alcohols, aldehydes, terpenes, norisoprenoids, fatty acids and phenols) present in Pinot noir (PN) wine. Four critical parameters including extraction solvent type, disperse solvent type, extraction solvent volume and disperse solvent volume were optimised with the aid of D-optimal design.

Far from complicated discussions on the relevant way to define « terroir », this article deals with the social perception that French young adults (aged from 18 to 30) have of this concept and the way it can help them to become wine consumers.

In this study two different compost types and two application methods were studied over 5 years (2009-2013) on mature Cabernet Sauvignon vines grown in a commercial vineyard in the AOC Piave area, northeastern Italy.

A critical aspect of wine quality from a consumer perspective is the overall impression of wine flavour, which is formed by the interplay of volatile aroma compounds, their precursors, and taste and matrix components. Grapes contribute some potent aroma compounds, together with a large pool of non-volatile precursors (e.g. glycoconjugates and amino acid conjugates). Aroma precursors can break down through chemical hydrolysis reactions, or through the action of yeast or enzymes, significantly changing the aroma profile of a wine during winemaking and storage. In addition, glycoconjugates of monoterpenes, norisoprenoids and volatile phenols, together with sulfur-conjugates in wine, provide a reservoir of additional flavour through the in-mouth release of volatiles which may be perceived retro-nasally.

Many high quality Open Source scientific applications have been available for a long time. Some of them have proved to be particularly useful for carrying out the usual activities involved in viticultural research projects, such as statistical analyses (including spatial analyses), GIS work, database management (possibly integrated with statistical and spatial analysis) and even “low-level” often highly time-consuming activities (e.g. repetitive task on text files).