Macrowine 2021
IVES 9 IVES Conference Series 9 Impact of varying ethanol and carbonation levels on the odor threshold of 1,1,6-trimethyl-1,2-dihydronaphtalene (petrol off-flavor) and role of berry size and Riesling clones

Impact of varying ethanol and carbonation levels on the odor threshold of 1,1,6-trimethyl-1,2-dihydronaphtalene (petrol off-flavor) and role of berry size and Riesling clones

Abstract

1,1,6-trimethyl-1,2-dihydronaphtelene (TDN) evokes the odor of “petrol” in wine, especially in the variety Riesling. Increasing UV-radiation due to climate change intensifies formation of carotenoids in the berry skins and an increase of TDN-precursors1. Exploring new viticultural and oenological strategies to limit TDN formation in the future requires precise knowledge of TDN thresholds in different matrices. Thresholds reported in the literature vary substantially between 2 µg/L up to 20 µg/L2,3,4 due to the use of different methods. As Riesling grapes are used for very different wine styles such as dry, sweet or sparkling wines, it is essential to study the impact of varying ethanol and carbonation levels. Therefore we determined the odor detection threshold (DT) with a three alternatives forced choice (3-AFC) test and calculated the best estimate threshold (BET) for each panelist, followed by the calculation of the BET for the whole panel. Matrices varied from water, a model wine, a dry Riesling wine and sparkling wine; the latter two exceptionally low in TDN. Carbonation in water, model wine and Riesling wine ranged from no addition to 2.5 and 6 bar pressure. Ethanol altered from 8 to 14% alc. in 2% alc. increments. Carbonation yielded an inconsistent effect due to a better volatilization leading to lower thresholds and masking of the TDN perception leading to slightly higher thresholds. Increasing ethanol levels however showed a clear tendency to raise the TDN thresholds, presumably due to better solubility and masking by its own pungent odor. Absolute thresholds varied in water between 2.6 and 4.0 µg/L and in wine between 8.5 and 15.2 µg/L. Since TDN is formed by a breakdown of carotenes, its occurrence correlates positively with the degree of sun exposure. This is partially governed by berry size and cluster density given by clonal differences. Thus we studied free and bound TDN in grapes and wines from 8 different Riesling clones. Furthermore, berries were divided in a two fractions according to a diameter smaller and larger than 13 mm. The berry fractions were separately crushed, pressed and fermented. Preliminary results indicate that the clonal influence on the formation of free TDN in the wines was substantially larger than the impact of berry size.

Literature: 1: Winterhalter, P; Goek, R. 2013; Carotenoid Cleavage Products. ACS Symp. Series 1134, 125-137. 2: Sacks, G. L.; Gates, M. J.; Ferry, F. X.; Lavin, E. H.; Kurtz, A. J.; Acree, T. E. 2012; Journal of Agricultural and Food Chemistry 60(12), 2998-3004 3: Ross, C. F.; Zwink, A. C.; Castro, L.; Harrison, R. 2014; Australian Journal of Grape and Wine Research 20(3), 335-339 4: Simpson, R. F. 1978; Chemistry and Industry 1, 37.

Publication date: May 17, 2024

Issue: Macrowine 2016

Type: Article

Authors

Michael Ziegler*, Hans-Georg Schmarr, Johanna Molenda, Recep Gök, Sandra Klink, Ulrich Fischer

*DLR Rheinpfalz

Contact the author

Tags

IVES Conference Series | Macrowine | Macrowine 2016

Citation

Related articles…

New molecular evidence of wine yeast-bacteria interaction unraveled by untargeted metabolomic profiling

Bacterial malolactic fermentation (MLF) has a considerable impact on wine quality. The yeast strain used for primary fermentation can consistently stimulate (MLF+ phenotype) or inhibit (MLF- phenotype) malolactic bacteria and the MLF process as a function of numerous winemaking practices, but the molecular evidence behind still remains a mystery. In this study, such evidence was elucidated by the direct comparison of extracellular metabolic profiles of MLF+ and MLF- yeast phenotypes. Untargeted metabolomics combining ultrahigh-resolution FT-ICR-MS analysis, powerful machine learning methods and a comprehensive wine metabolite database, discovered around 800 putative biomarkers and 2500 unknown masses involved in phenotypic distinction.

Using elicitors in different grape varieties. Effect over their phenolic composition

Phenolic compounds are very important in crop plants and have been the subject of a large number of studies. Three main reasons can be cited for optimizing the level of phenolic compounds in crop plants: their physiological role in plants, their technological significance for food processing, and their nutritional characteristics1 Indeed, an enormous diversity of phenolic antioxidants is found in fruits and vegetables, and their presence and roles can be affected or modified by several pre- and postharvest cultural practices and/or food processing technologies (Ruiz-García et al. 2012, Goldman et al. 1999, Tudela et al. 2002). In winegrapes, the technological importance of phenolic compounds, mainly flavonoids, is well-known.

Defining the mechanisms and impact of winemaking treatments on tannin and polysaccharides in red wine: recent progress in creating diverse styles

Tannin and polysaccharide concentration and composition is important in defining the texture of red wines, but can vary due to factors such as cultivar, region, grape ripeness, viticultural practices and winemaking techniques. However, the concentration and composition of these macromolecules is dependent not only on grape tannin and polysaccharide concentration and composition, but also their extractability and, in the case of polysaccharides, their formation by yeast. Through studies into the influence of grape maturity, winemaking and sensory impacts of red grape polysaccharides, seed and skin tannins, recent research in our laboratory has shown that the processes involved in the extraction of these macromolecules from grapes and their retention in wine are very complex.

Metabolomics of grape polyphenols as a consequence of post-harvest drying: on-plant dehydration vs warehouse withering

A method of suspect screening analysis to study grape metabolomics, was developed [1]. By performing ultra-high performance liquid chromatography (UHPLC) – high-resolution mass spectrometry (HRMS) analysis of the grape extract, averaging 320-450 putative grape compounds are identified which include mainly polyphenols. Identification of metabolites is performed by a new HRMS-database of putative grape and wine compounds expressly constructed (GrapeMetabolomics) which currently includes around 1,100 entries.

Mean polymerization degree of proanthocyanidins of grape seeds, skins and wines from Agiorgitiko (cv. Vitis vinifera): Differences among vintages

Grape phenolic compounds are very important constituents of red wine because, in addition to their antioxidant properties, they contribute to color, astringency and bitterness, oxidation reactions, interactions with proteins and ageing behavior of wines. The aim of our study was to assess the structural characteristics of grape and wine proanthocyanidins of Agiorgitiko variety and to evaluate the influence of the vintage year. Twelve vineyard locations were designated in the Nemea wine region. For three consecutive years (2012-2014), the grapes were harvested at technological maturity and the method of phloroglucinolysis was employed to determine the mean degree of polymerization (mDP) and subunit composition of the samples.