Macrowine 2021
IVES 9 IVES Conference Series 9 Effect of intra‐vineyard ripeness variation on the efficiency of commercial enzymes on berry cell wall deconstruction under winemaking conditions

Effect of intra‐vineyard ripeness variation on the efficiency of commercial enzymes on berry cell wall deconstruction under winemaking conditions

Abstract

Intra-vineyard variation grape berry ripening occurs within bunches, between bunches on the same vine and between vines. Although it is assumed that such variation also occurs at the grape berry cell wall level, no study to data has investigated in any depth. Here we have used a intra-vineyard panel design to investigate pooled bunches from six vines (per panel) in the context of a winemaking scenario. The dissected vineyard was harvested by separate panels, where each panel was then subjected to a standard winemaking procedure with or without the addition of three different enzyme preparations for maceration. Adjacent untreated panels acted as the enzyme controls. Hence we combined two studies into one design. Cell wall material harvested from the treated and untreated panels were subjected to high throughput cell wall profiling tools combined with multivariate data analysis. The study showed that significant variation at the cell wall polymer level occurred across the vineyard amongst the different panels. Furthemore, all enzyme applications had a strong and clear effect in reducing this variation through de-pectination. What was most interesting is that while de-pectination occurred the levels of esterification were unaffected by the enzymes. This is a positive for wine quality as no methanol or acetates would have been produced from the de-pectination and not all natural grape berry variation is affected. This study provides clear evidence that enzymes can positively influence the consistency of winemaking without necessarily removing all variability provided from the vineyard. This study provides a foundation for further research into the relationship with grape berry cell wall architecture and enzyme formulations.

Publication date: May 17, 2024

Issue: Macrowine 2016

Type: Poster

Authors

Yu Gao*, John Paul Moore, Jonatan Fangel, Melane Vivier, William Willats

*Institute for Wine Biotechnology

Contact the author

Tags

IVES Conference Series | Macrowine | Macrowine 2016

Citation

Related articles…

How do different oak treatment affect the sensory composition of Chenin blanc wines over time?

Wooden barrels have been the preferred method for oak maturation for wines, but the use of alternative oak products, such as staves and oak chips have increased in South Africa due to lower production costs. This study investigated the effect of different oak products used during fermentation and ageing on the sensory profile, degree of liking and perceived quality of a South African Chenin blanc wine. The different wine treatments included an unoaked tank control wine, wines matured in 5th fill barrels, wines matured in new barrels from three different cooperages, and wines matured in 5th fill barrels with stave inserts from two different cooperages.

Prediction of the production kinetics of the main fermentative aromas in alcoholic fermentation

Fermentative aromas (especially esters and higher alcohols) highly impact the organoleptic profile of young and white wines. The production of these volatile compounds depends mainly on temperature and Yeast Available Nitrogen (YAN) content in the must. Available dynamic models predict the main reaction
(bioconversion of sugar into ethanol and CO2 production) but none of them considers the production kinetics of fermentative aroma compounds during the process of fermentation. We determined the production kinetics of the main esters and higher alcohols for different values of initial YAN content and temperature, using an innovative online monitoring Gas Chromatography device.

Estimation of chemical age of red wines with the use of Fourier transform infrared spectroscopy (FT-IR) and chemometrics

The color of a red wine is one of the most important parameters of its quality, giving much information on its status, such as the grape variety used or the winemaking style. As the result of a complex equilibrium between different forms of anthocyanins and polymerization reactions which occur over the course of time, color can also serve as an indication of a wines’ age. For this purpose the “chemical age” i and ii indexes have been introduced by Somers in 1977. The chemical age index i measures the color absorbance after the addition of acetaldehyde while chemical index ii provides an indication of how much of the total red pigments are resistant to SO2 bleaching.

Impact of smoke exposure on the chemical composition of grapes

Vineyard exposure to smoke can lead to grapes and wine which exhibit objectionable smoky and ashy aromas and flavours, more commonly known as ‘smoke taint’ [1, 2]. In the last decade, significant bushfires have occurred around the world, including near wine regions in Australia, Canada, South Africa and the USA, as a consequence of the warmer, drier conditions associated with climate change. Considerable research has subsequently been undertaken to determine the chemical, sensory and physiological consequences of grapevine exposure to smoke. The sensory attributes associated with smoke-tainted wine have been linked to the presence of several smoke-derived volatile phenols, such as guaiacols, syringols and cresols [2].

Evaluation of Polarized Projective Mapping as a possible tool for attributing South African Chenin blanc dry wine styles

Multiple Factor Analysis (MFA) According to the Chenin blanc Association of South Africa, there are three recognized dry wine styles, Fresh and Fruity (FF), Rich and Ripe Unwooded (RRU), and Rich and Ripe Wooded (RRW), classically attributed with the help of sensory evaluation. One of the “rapid methods” has drawn our attention for the purpose of simplifying and making style attribution for large sample sets, evaluated during different sessions, more robust. Polarized Projective Mapping (PPM) is a hybrid of Projective Mapping (PM) and Polarised Sensory Positioning (PSP). It is a reference-based method in which poles
(references) are used for the evaluation of similarities and dissimilarities between samples.