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Record W2610431822 · doi:10.1021/acs.jafc.7b01233

Impact of Bottle Aging on Smoke-Tainted Wines from Different Grape Cultivars

2017· article· en· W2610431822 on OpenAlexfundno aff
Renata Ristić, Lieke van der Hulst, Dimitra L. Capone, Kerry L. Wilkinson

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersAustralian Research CouncilWine AustraliaAlberta Water Research Institute
KeywordsWineSmokeFood scienceGuaiacolBottleChemistryAromaVineyardWine colorFlavorPhenolsWhite WineOrganic chemistryHorticultureBiology

Abstract

fetched live from OpenAlex

Smoke taint is the term given to the objectionable smoky, medicinal, and ashy characters that can be exhibited in wines following vineyard exposure to bushfire smoke. This study sought to investigate the stability of smoke taint by determining changes in the composition and sensory properties of wines following 5 to 6 years of bottle aging. Small increases in guaiacol and 4-methylguaiacol (of up to 6 μg/L) were observed after bottle aging of smoke-affected red and white wines, while syringol increased by as much as 29 μg/L. However, increased volatile phenol levels were also observed in control red wines, which indicated that changes in the composition of smoke-affected wines were due to acid hydrolysis of conjugate forms of both naturally occurring and smoke-derived volatile phenols. Acid hydrolysis of smoke-affected wines (post-bottle aging) released additional quantities of volatile phenols, which demonstrated the relative stability of glycoconjugate precursors to the mildly acidic conditions of wine. Bottle aging affected the sensory profiles of smoke-affected wines in different ways. Diminished fruit aroma and flavor led to the intensification of smoke taint in some wines, but smoke-related sensory attributes became less apparent in smoke-affected Shiraz wines, post-bottle aging.

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)0.000
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 score0.826
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.256
Teacher spread0.232 · 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

Citations47
Published2017
Admission routes1
Has abstractyes

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