IVAS 2022 banner
IVES 9 IVES Conference Series 9 IVAS 9 IVAS 2022 9 Interpreting wine aroma: from aroma volatiles to the aromatic perception

Interpreting wine aroma: from aroma volatiles to the aromatic perception

Abstract

Wine contains so many odorants that all its olfaction-related perceptions are, inevitably, the result of the interaction between many odorants. This natural complexity makes that the study of wine aroma has to deal not only with the quantitative determination of a large group of odorants, but has also to understand the basic principles determining the interactions between odorants. The basic mechanisms of odour interactions are not well known and seem to be very complex, but taking as base classical studies did by psychophysicists in the last 50 years, some outcomes of flavour chemistry, and some basic elements of the theory of perception, it has been recently possible to propose a systematic classification of odour interactions into four different categories: competitive, cooperative, destructive and creative. 
Competitive interactions take place when two or more non-blending odours are simultaneously perceived. The perceived intensity of any of them decreases as the odour intensities of other of the components is increased. Cooperative interactions take place when many odorants are present at subthreshold levels and are particularly relevant when similar odorants are present at whatever odour intensities. In these last cases, these interactions lead to the formation of odour vectors, which are groups of odorants of similar aroma acting concertedly and translating to the final product a specific aroma feature.  Destructive interactions take place when one of the odours present in the mixture is able to deconfigure the odour perception of the others, bringing about a decrease in the odour intensity before the deconfiguring odour is perceived. Most wine off-odours belong into this category. Creative interactions are configurational processes and take place when a new odour emerges out of the mixture of odorants. In milder cases, the addition of one odorant boosts the intensity of the others present in the mixture.
With these elements at hand, it is possible to propose a systematic to understand the chemical bases of wine aroma perceptions. Overall, around 80 aroma molecules, seem to be able to explain the different positive aroma nuances of all wines. The major wine volatile components, all of them by-products of alcoholic fermentation, form “the wine aroma buffer”, which is a mixture with vinous aroma and a strong deconfigurational power induced by the destructive interactions elicited by ethanol, isoamyl and isobutyl alcohols and acetic acid. Then, wine odorants are further classified into 35 different aroma vectors, broadly classified into 10 different odour categories. Some creative interactions, leading to relevant wine odours, such as pineapple, strawberry candy, black fruits or raisins have been also identified and will be discussed.

DOI:

Publication date: June 23, 2022

Issue: IVAS 2022

Type: Article

Authors

Vicente Ferreira¹

¹Laboratory for Aroma Analysis and Enology (LAAE)

Contact the author

Keywords

wine aroma, flavor, odorant, perceptual interaction

Tags

IVAS 2022 | IVES Conference Series

Citation

Related articles…

Investigating the Ancient Egyptian wines: The wine jars database

In Ancient Egypt, wine was a luxury product consumed mainly by the upper classes and the royal family and offered to gods in daily religious rituals in the temples.
Since the Predynastic (4000-3100 BC) period, wine jars were placed in tombs as funerary offerings. From the Old Kingdom (2680-2160 BC) to the Greco-Roman (332 BC-395 AD) period, viticulture and winemaking scenes were depicted on the private tombs’ walls. During the New Kingdom (1539-1075 BC), wine jars were inscribed to indicate: vintage year, product, quality, provenance, property and winemaker’s name and title.

How artificial intelligence (AI) is helping winegrowers to deal with adversity from climate change

Artificial intelligence (AI) for winegrowers refers to robotics, smart sensor technology, and machine learning applied to solve climate change problems. Our research group has developed novel technology based on AI in the vineyard to monitor vineyard growth using computer vision analysis (VitiCanopy App) and grape maturity based on berry cell death to predict flavor and aroma profiles of berries and final wines.

Clone performance under different environmental conditions in California

Clonal evaluation of winegrapes in California has not been extensive. Early selection work by Alley (1977), Olmo (unpublished data) and Goheen (personal communication) resulted in the current collection

Estudios de zonificación vitícola en España

La delimitación y caracterización de zonas vitícolas plantea en España problemas específicos no sólo por las características peculiares del territorio sino también por el tamaño

Carry over effect of shoot trimming and deficit irrigation on fruit yield and berry total soluble solids

The increase in air temperature that is occurring in many important wine-growing areas around the world is resulting in the decoupling between the phenolic and the technological maturity of grapevine berries. This new ripening pattern leads to the production of light-bodied high alcoholic wines, but this is in countertendency with the increasing consumers’ demand for wines with low-to-mid alcohol concentrations. The oenological techniques proposed to reduce wine alcohol content are often very expensive and lead to detrimental effects on wine quality. Many viticultural practices have been proposed to slow down sugar accumulation the berry. One possible strategy that was previously found to be suitable for Aglianico grapevine is post-veraison shoot trimming. The aim of this work was to assess the carry over effects on the following year of shoot trimming and vine water status on yield and total soluble solids because the expected reduction in vine fertility could lead to a reduction in the effectiveness of shoot trimming.