WAC 2022 banner
IVES 9 IVES Conference Series 9 WAC 9 WAC 2022 9 3 - WAC - Posters 9 Aroma diversity of Amarone commercial wines

Aroma diversity of Amarone commercial wines

Abstract

Amarone is an Italian red wine produced in the Valpolicella area, in north-eastern Italy. Due to its elaboration with withered grapes, Amarone is a rather unique example of dry red wine. However, there is very limited data so far concerning the volatile composition of commercial Amarone wines, which also undergo a cask aging of 2-4 years before release. The present work aims at characterizing the aroma composition of Amarone and to elucidate the relationships between chemical composition and sensory characters. Two sets of Amarone wines from different vintages 2015 (17 wines) and 2016 (15 wines) were analyzed. The analyses were carried out by means of Gas Chromatography-Mass Spectrometry (GC-MS) and extracted by Solid Phase Extraction (SPE) and Solid Phase Micro Extraction (SPME). In addition, the sampled wines were subjected to a sensory evaluation in the form of sorting task. From both data sets, 70 volatile compounds were successfully identified and quantified, 30 of which were present in concentrations above their odor thresholds in all the samples. Using the odor activity value (OAV), the compounds that potentially contribute to Amarone perceived aroma are β-damascenone, ethyl and isoamyl acetate, ethyl esters (hexanoate, octanoate, butanoate, 3-methybutanoate), 4-ethyl guaiacol, 3-methylbutanoic acid, dimethyl sulfide (DMS), eugenol, massoia lactone, 1,4-cineol, TDN, cis-whisky lactone. The only differences found between the two vintages’ OAV list, could be observed in the presence of dimethyl trisulfide (DMTS) in the vintage 2015; whereas in the 2016 set γ-nonalactone and trans-whisky lactone were found. Regarding the compounds that impart the most differences across both vintages, OAV max/min, where 4- ethyl phenol, 4-ethyl guaiacol, 1,8-cineole, 1,4-cineole, dimethyl sulfide (DMS). Results from the sorting task sensory analysis of the 17 wines from vintage 2015 showed three clusters formed. Cluster 1 composed of eight wines and described as “red fruit”, “solvent” and “sweet spices”. Cluster 2 formed by four Amarone was associated mainly with the “animal” and “oak/toasted” attributes. And cluster 3 (five wines) described with the attribute “cooked fruit”. While in the sorting task of vintage 2016 (15 wines) two vintages coming from different wineries . Moreover, from the volatiles analyzed, compounds such as dimethyl sulfide (DMS) and cineoles have been singled out as potential aroma markers of diversity in Amarone wines.

DOI:

Publication date: June 27, 2022

Issue: WAC 2022

Type: Article

Authors

Jessica Anahi Samaniego Solis, Maurizio, Ugliano, Davide, Slaghenaufi, Giovanni, Luzzin

Presenting author

Jessica Anahi Samaniego Solis – University of Verona

University of Verona | University of Verona | University of Verona

Contact the author

Keywords

Amarone – grape withering – Corvina – Corvinone

Tags

IVES Conference Series | WAC 2022

Citation

Related articles…

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.

Sensitivity of vis‐nir spectral indices to detect nitrogen deficiency and canopy function in cv. Barbera (Vitis vinifera L.) Grapevines

Precision nutrient management in viticulture can be addressed on the basis of a spatial characterization of within‐vineyard vine

A novel approach for the identification of new biomarkers of wine consumption in human urine using untargeted metabolomics

Wine is one of the most representative components of Mediterranean diet. Moderate wine intake together with food, has been positively correlated with reduced risk of many chronic diseases. This beneficial effect seems to be ascribed to elevated polyphenolic content of wine [1]. Traditional approaches for the identification of wine biomarkers consumption include targeted metabolomics that focuses on the quantification of well-defined metabolites, losing a valuable information about a massive number of compounds. On the other hand, untargeted metabolomics can disclose a large quantity of signals corresponding to potential biomarkers in a single analysis with high sensitivity and resolution.

Evaluation of intravarietal variability and selection for tolerance to downy mildew: The case of Antão Vaz variety in Portugal 

Antão Vaz is a Portuguese white grapevine variety grown mainly in the wine-growing regions of Southern Portugal, particularly in the Alentejo, Lisbon and Setúbal peninsula regions. It is a very vigorous and productive variety, giving the wines a strong identity. It needs heat and sunlight and prefers deep and dry soils, which makes it tolerant to scald caused by the high summer temperatures of Southern Portugal. However, this variety is very susceptible to downy mildew, caused by plasmopara viticola, a very destructive disease in years with rainy springs.

Fully automated non-targeted GC-MS data analysis

Non-targeted analysis is applied in many different domains of analytical chemistry such as metabolomics, environmental and food analysis. In contrast to targeted analysis, non-targeted approaches take information of known and unknown compounds into account, are inherently more comprehensive and give a more holistic representation of the sample composition.