Enoforum 2021
IVES 9 IVES Conference Series 9 Enoforum Web 9 Enoforum Web Conference 2021 9 Cellar session 9 Impact of chitosan treatment on the physico-chemical features of a sangiovese red wine

Impact of chitosan treatment on the physico-chemical features of a sangiovese red wine

Abstract

Chitosan is gaining interest in red winemaking thanks to its ability to inhibit the development of Brettanomyces spp. yeast, or other undesired wine microbial threats. However, little is known about potential side-effects of its addition on the physico-chemical parameters of red wines. To fill the gap on this subject, this work focused on changes in color, phenolic and volatile composition of red wines treated for 7 days with 0.5 g/L of fungoid chitosan, added in both undissolved and dissolved form. When compared to untreated samples, minor changes in phenolic compounds were observed in chitosan added wines, mainly involving hydroxycinnamic acids and flavonols, with reductions of 3 mg/L and 1.5 mg/L respectively. Ellagic acid, however, was absorbed up to 2 mg/L, which reduced his content by 40%. Since some of these compounds actively participate to co-pigmentation with anthocyanins, the color of wines was influenced accordingly. Chitosan marginally absorbed some aroma compounds, including ethyl esters and volatile phenols whose amounts were slightly but significantly decreased after treatment. Visual and olfactive comparison of samples confirmed that, at the dose adopted, chitosan is suitable to be used in red winemaking for microbial or physical stability purposes, not severely impairing the quality parameters of the final wines.

DOI:

Publication date: April 23, 2021

Issue: Enoforum 2021

Type: Article

Authors

Antonio Castro Marin, Fabio Chinnici

Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 40, 40127

Contact the author

Tags

Enoforum 2021 | IVES Conference Series

Citation

Related articles…

Peptides diversity and oxidative sensitivity: case of specific optimized inactivated yeasts

Estimation of the resistance of a wine against oxidation is of great importance for the wine. To that purpose, most of the commonly used chemical assays that are dedicated to estimate the antioxidant (or antiradical) capacity of a wine consist in measuring the capacity of the wine to reduce an oxidative compound or a stable radical.

qNMR metabolomics a tool for wine authenticity and winemaking processes discrimination

qNMR Metabolomic applied to wine offers many possibilities. The first application that is increasingly being studied is the authentication of wines through environmental factors such as geographical origin, grape variety or vintage (Gougeon et al., 2019).

Advances in phenology modelling of the grapevine

Historical records of grapevine phenology have been collected over decades throughout different winegrowing regions. These records have demonstrated advances in key developmental stages such as budburst, flowering and veraison because of increased temperatures due to climate change.

Bacterial community in different wine appellations – biotic and abiotic interaction in grape berry and its impact on Botrytis cinerea development

An in-depth knowledge on the conditions that trigger Botrytis disease and the microbial community associated with the susceptibility/resistance to it could led to the anticipation and response to the Botrytis emergence and severity. Therefore, the present study pretends to establish links between biotic and abiotic factors and the presence/abundance of B. cinerea.

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.