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Record W7024438284

Selective use of winemaking supplements to modulate the chemical composition and sensory properties of Shiraz wine

2017· dissertation· en· W7024438284 on OpenAlexfundno aff

Bibliographic record

VenueAdelaide Research & Scholarship (AR&S) (University of Adelaide) · 2017
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersWine AustraliaAlberta Water Research Institute
KeywordsWineWinemakingMouthfeelTanninOrganolepticAging of wineAroma of wineMaceration (sewage)Flavour
DOInot available

Abstract

fetched live from OpenAlex

A global trend of increasing alcohol strength in table wine has emerged over the past four decades, largely due to advanced grape maturity associated with climate change. Harvesting grapes before they reach full maturity, i.e. at lower total soluble solids, can be an undemanding and effective method to control alcohol levels in wine. However, fruit maturity has a significant influence on wine composition. Wines made from early harvested fruit can be deficient in the desirable organoleptic characters usually associated with wines made from mature fruit, such as aroma and flavour intensity, as well as mouthfeel attributes. The current project therefore aims to improve the quality of Shiraz wines made from early harvested fruit, through selective application of commercial winemaking supplements. A critical review of literature showed that compared to mature fruit, early harvested fruit has considerably lower tannin and mannoprotein concentrations; i.e. wine constituents that are associated with important mouthfeel attributes, such as astringency and viscosity. To address these deficiencies, three supplements that are legally permitted for use in Australian wine production, i.e. a maceration enzyme, an oenotannin and a mannoprotein product, were selected based on their potential for modifying wine tannin and polysaccharide compositions. These products were added during the vinification process of Shiraz wines produced from early harvested grapes, either individually or in combination. The resultant wines were compared with Shiraz wines made from mature fruit, in terms of both chemical composition and sensory characters. The results showed that modifying tannin and polysaccharide composition could indeed alter the perception of astringency. Furthermore, the combined use of mannoprotein and oenotannin additives resulted in a wine that closely resembled the sensory properties of wines made from mature fruit. The warmer than usual vintage conditions experienced, variation observed in supplement composition, and recovery of additives in treated wines, represented limitations of this study. Thus, three subsequent studies were designed to further explore the effect of additives in more depth. Fourteen grape based oenotannins and eight mannoproteins were sourced from commercial suppliers in the Australian market. The aim was to understand the compositional variation amongst products, and by extension, the different effects likely to be achieved through product selection. Substantial variation was observed amongst products of both types of supplements. Some products showed good agreement between their composition and the designated material of origin, whereas others showed significant differences. Based on results from this study, three commercial products, two oenotannins (derived from grape skin and seed respectively) and one mannoprotein were selected, as these products were similar in composition to their counterparts isolated from grape and wine; additives were further characterised in two subsequent studies. The selected products were introduced into two finished Shiraz wines of 11.5% and 14.5% v/v alcohol content, i.e. wines made from fruit of early and later harvests, respectively. The same supplementation regimes were applied to both wines, and thus established a series of wines comprising different ethanol, tannin and polysaccharide concentrations and/or compositions. The aim was to evaluate, using the sensory analysis techniques, changes in astringency and body (viscosity) mouthfeel characters, attributable to the additives and/or their interactions. However, the judges involved in sensory evaluation could not perceive any variation in astringency resulting from the differences in tannin concentration and composition imparted by the additives. Furthermore, although the judges could perceive differences in wine body between wines of the two harvests, they could not perceive any effects of mannoprotein addition, even at dose rates 2.5 times higher than the level legally permitted in Australia. It was not immediately obvious if the lack of sensory discrimination was due to subtle differences amongst samples or a lack of sensitivity from the judging panel. Finally, the addition of supplements is expected to influence the colloidal state of wine, which may in turn affect wine stability and sensory characters. To test this hypothesis, two polysaccharides, a mannoprotein and an arabinogalactan, were purified from two commercial products, and combined with a tannin fraction purified from grape seeds, in model wine solutions of 12% and 15% v/v ethanol concentrations. The formation of aggregates between polysaccharides and tannins was explored using a novel technique, nanoparticle tracking analysis (NTA); with results confirmed using dynamic light scattering and UV-visible spectroscopy techniques. The behaviour of the two polysaccharides towards tannin was substantially different. Mannoprotein formed large, highly light scattering aggregates with tannin, while arabinogalactan gave weak interactions with tannin and formed low-intensity light scattering aggregates. The 3% difference in alcohol content was sufficient to modify aggregation between mannoproteins and tannin. The implications for wine colloidal properties are discussed based on these results. The collective findings of this research offers insights into the compositional variabilities of commercial winemaking supplements, as well as their effects on wine macromolecule composition, colloidal state and sensory properties. The knowledge gained from these studies can inform winemakers’ selection and use of winemaking supplements, especially with regards to improving the quality of red wines made from early harvested grapes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.157
GPT teacher head0.326
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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