IVAS 2022 banner
IVES 9 IVES Conference Series 9 IVAS 9 IVAS 2022 9 New Insights into Wine Color Analysis: A Comparison of Analytical Methods and their Correlation with Sensory Perception

New Insights into Wine Color Analysis: A Comparison of Analytical Methods and their Correlation with Sensory Perception

Abstract

Two spectrophotometric methods are recommended by the Organisation Internationale de la vigne et du vin (OIV). The first is the method after Glories, were the absorbances at 420 nm, 520 nm and 620 nm are measured (OIV 2006a). The second method, from the Commission Internationale de L’Eclairage (CIE), uses the entire spectrum from 300 nm to 800 nm to calculate the CIE Parameters L*, a*, and b*. While the OIV recommends a data interval of 5 nm for the CIE method, no such recommendations are given for other parameters such as the scan speed (OIV 2006b). To investigate the parameter settings wines from a dark red grape variety (Merlot), a light red grape variety (Vernatsch) and a white grape variety (Chardonnay) were measured with different data intervals and scan speeds.
Results indicate that the scan speed and data interval have significant impact on the color measurement and the accuracy is dependent from the lightness of a wine. Since both, the Glories system and the CIE L*a*b* system, are widely used in wine analysis it is important to know if those systems are comparable. With the analytical results in mind the correlation has to be conducted for dark red wines, light red wines and white wines. The analysis of 112 wines (56 red wines and 56 white wines) from different grape varieties, origins, and vintages, using both the Glories and CIE methods revealed that the correlation between
the two methods is only possible for dark red wines. Furthermore it is unclear which of the methods are more consonant with the sensory perception. Due to the lack of standardisation a new method of color evaluation was developed. The CIE L*a*b* system better reflects sensory perception than the Glories system, but both systems cannot describe every facet of wine color

References

OIV (2006)a. Determination of chromatic characteristics according to CIELab, Method OIV-MA-AS2-07B. COMPENDIUM OF INTERNATIONAL ANALYSIS OF METHODS, OIV.
OIV (2006)b. Determination of chromatic characteristics according to CIELab, Method OIV-MA-AS2-11. COMPENDIUM OF INTERNATIONAL ANALYSIS OF METHODS, OIV.

DOI:

Publication date: June 23, 2022

Issue: IVAS 2022

Type: Article

Authors

Hensel Marcel¹, Scheiermann Marina¹and Durner Dominik¹

¹Institute for Viticulture and Oenology, Dienstleistungszentrum Ländlicher Raum (DLR) Rheinpfalz

Contact the author

Keywords

color analysis, color spaces, Glories, spectrophotometry

Tags

IVAS 2022 | IVES Conference Series

Citation

Related articles…

Advances in the chemistry of rosé winemaking and ageing

The market share of Rosé wine in France has grown from 11 % to 32 % over the last 20 years. Current trends are towards rosé wines of a lighter shade of pink, and where possible, containing a greater concentration in varietal thiols. Grape varieties, the soil on which they are grown, viticultural practices and winemaking technology all impact the polyphenols, color and aromas of rosé wines.

Effects of environmental factors and vineyard pratices on wine flora dynamics

he intensification of t vineyard practices led to an impoverishment of the biological diversity. In vineyard management, the reflection to reduce pesticides uses concerns mainly the soil management of the vineyard, and often focuses on flora management in the inter-row.

Automated detection of downy mildew in vineyards using explainable deep learning

Traditional methods for identifying downy mildew in commercial vineyards are often labour-intensive, subjective, and time-consuming.

Learning from remote sensing data: a case study in the Trentino region 

Recent developments in satellite technology have yielded a substantial volume of data, providing a foundation for various machine learning approaches. These applications, utilizing extensive datasets, offer valuable insights into Earth’s conditions. Examples include climate change analysis, risk and damage assessment, water quality evaluation, and crop monitoring. Our study focuses on exploiting satellite thermal and multispectral imaging, and vegetation indexes, such as NDVI, in conjunction with ground truth information about soil type, land usage (forest, urban, crop cultivation), and irrigation water sources in the Trentino region in North-East of Italy.