Terroir 1996 banner
IVES 9 IVES Conference Series 9 Enological characters of thirty vines in four different zones of Tuscany

Enological characters of thirty vines in four different zones of Tuscany

Abstract

In the last few years the development of HPLC techniques together with multivariate statistical methods allowed to set methodics of large discriminant and classing efficacy in the study of wine-grapes.
The phenolic compounds (cynnamic acids and anthocyanidins) in thirty different wines grown in 4 different zones of Tuscany (Arezzo, Grosseto, Pisa and Lucca) have been analyzed by HPLC.
The analytical data were statistical worked out by two analysis ACP and a linear discriminant analisys in order to discriminate the four zones, using Fisher linear function.
The stepwise technique, to choose variables, pointed out the delphinidin-g, the peonidin-g, the ratio of three/two-sostituited anthocyanines, the sum of cis and trans-cutaric acids, the caffeic acid and the ratio of caffeic acid and the sum of cutaric acids among the most important.
Then we worked out 6 comparisons between two zones and exactly AR/LU, AR/PI, AR/GR, LU/PI, LU/GR and PI/GR.
The environment discriminant threshold, the differences, the discriminant functions of vine-variety in every zone and the measure of discrimination errors were obtained.
Therefore a vinevariety-environment interaction is quite probable.

DOI:

Publication date: February 24, 2022

Issue: Terroir 2000

Type: Article

Authors

A.Piracci, P.Storchi, P.Bucelli, F. Giannetti, V. Faviere

Istituto Sperimentale per l’Enologia ​Via di Vertine 1 ​53013 Gaiole in Chianti (SI)
Istituto Sperimentale per la Viticoltura – Arezzo

Contact the author

Tags

IVES Conference Series | Terroir 2000

Citation

Related articles…

Terpenoid profiles and biosynthetic gene expression pattern in Asti DOCG white muscat grapes at ripening as affected by different canopy management protocols

Aim: The main goal of this study was to find an efficient canopy management to limit the high temperature-related aroma losses of White Muscat grapes, and consequently to preserve the quality standards of Asti DOCG wines.

Exploring between- and within-vineyard variability of “Malvasia di Candia aromatica” vineyards from Colli Piacentini

Several studies demonstrated how climate and soil may be key drivers of variability at different scales.

YEAST DERIVATIVE PRODUCTS: CHARACTERIZATION AND IMPACT ON RIBOFLAVIN RELEASE DURING THE ALCOHOLIC FERMENTATION

Light-struck taste (LST) is a wine fault that can occur in white and sparkling wines when exposed to light. This defect is mainly associated to the formation of methanethiol and dimethyl disulfide due to light-induced reactions involving riboflavin (RF) and methionine [1]. The presence of RF in wine is mainly due to the metabolism of yeast [2] which fermenting activity can be favoured by using yeast derivative products (YDPs) as nutrients. Nonetheless, a previous study showed the addition of YDPs before the alcoholic fermentation (AF) led to higher concentrations of RF in wines [3]. Due to the widespread use of YDPs in the winemaking process, this study aimed to understand the possible relation between the content of RF in wine and the YDP adopted as nutrient for AF.

A new graphical interface as a tool to integrate data from GC-MS and UPLC-MS-QTOF: new compounds related with port wine aging

Port wine value is related to its molecular profile resulting from the changes occurring during the ageing period. It is of empirical knowledge that the style is greatly affected by the oxidation regimens, i.e. bottle versus barrel storage

Strategies for sample preparation and data handling in GC-MS wine applications

It is often said that wine is a complex matrix and the chemical analysis of wine with the thousands of compounds detected and often measured is proof. New technologies can assist not only in separating and identifying wine compounds, but also in providing information about the sample as a whole. Information-rich techniques can offer a fingerprint of a sample (untargeted analysis), a comprehensive view of its chemical composition. Applying statistical analysis directly to the raw data can significantly reduce the number of compounds to be identified to the ones relevant to a particular scientific question. More data can equal more information, but also more noise for the subsequent statistical handling.