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Record W3212418121 · doi:10.1111/ajgw.12527

Autolysis and the duration of ageing on lees independently influence the aroma composition of traditional method sparkling wine

2021· article· en· W3212418121 on OpenAlexfundno aff
Samantha Sawyer, Rocco Longo, Mark Solomon, Luca Nicolotti, H Westmore, Angela Merry, G Gnoinski, A Ylia, Robert G. Dambergs, F Kerslake

Bibliographic record

VenueAustralian Journal of Grape and Wine Research · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersWine AustraliaAlberta Water Research Institute
KeywordsLeesWineAutolysis (biology)AromaAroma of wineFood scienceChemistryBottling lineComposition (language)AgeingAging of wineWine colorWhite WineBiologyBiochemistryArt

Abstract

fetched live from OpenAlex

Background and Aims: Yeast autolysis is understood to be primarily responsible for giving traditional method sparkling wines complex and developed aromas. The contribution from ageing the wine itself, however, is less well-established. This study aimed to determine the contribution of autolysis products and compounds associated with wine oxidation and ageing in Vitis vinifera L. Chardonnay and Pinot Noir wines over 24 months. Methods and Results: Chardonnay and Pinot Noir base wines were tiraged, or aged with and without primary lees. Volatile composition analyses (HS-SPME/GC/MS and GC/MS/MS) were conducted at 6, 12, and 24 months post-bottling and sensory appraisals at 12 and 24 months. The duration of ageing significantly influenced compositional changes in fermentation-derived and oxidative-flavour-associated compounds. Ageing base wines off or on lees produced similar maturation-associated aroma profiles to sparkling wines irrespective of cultivar. Conclusions: The contribution of autolysis products did not feature as strongly as anticipated over 24 months, indicating that compounds associated with wine ageing primarily influenced the aroma of mature sparkling wines. This finding suggests winemakers ageing their wines on lees for 24 months or less should place more emphasis on base wine composition to manipulate the aroma profiles of sparkling wines. Significance of the Study: First comparative chemical compositional study of base wines concurrently with sparkling wines.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.170

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.112
GPT teacher head0.353
Teacher spread0.241 · 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

Citations30
Published2021
Admission routes1
Has abstractyes

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