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Record W1543375379 · doi:10.1111/ajgw.12140

Yeast-induced changes in the concentration and structure of oligomeric proanthocyanidins during simulated wine fermentation

2015· article· en· W1543375379 on OpenAlexfundno aff
Jing Li, Hongwei Zhao, Weidong Huang

Bibliographic record

VenueAustralian Journal of Grape and Wine Research · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersChina Scholarship CouncilNational Natural Science Foundation of ChinaQingdao Agricultural UniversityAlberta Water Research Institute
KeywordsWinemakingWineFermentationProanthocyanidinYeastYeast in winemakingFood scienceComposition (language)ChemistryFermentation in winemakingStrain (injury)TanninSaccharomyces cerevisiaeBiochemistryBiologyPolyphenol

Abstract

fetched live from OpenAlex

Background and Aims Proanthocyanidins (PAs), the predominant phenolic substances in wine, are responsible for the astringency of wine. The present work aims to understand the effect of yeast on the concentration and composition of the subunits of PAs during fermentation. Methods and Results The concentration and cleavage products (subunits) of PAs were measured during fermentation. Early in fermentation (within about 4 days), the concentration of PAs decreased significantly by around 78% for strain BH8 and 67% for strain AWRI R2; in addition, the mean degree of polymerisation and composition of PAs underwent considerable change. At the later phase of fermentation, no significant change was recorded in the concentration and mean degree of polymerisation of PAs, whereas the composition of PAs evolved progressively. Conclusions Yeast can exert a significant influence on the concentration and structure of PAs during wine fermentation in a strain- and time-dependent manner. Significance of the Study The results will enable a better understanding of the effect of yeast on the evolution of PAs in winemaking, as well as increase knowledge of the interaction between yeasts and phenolic substances to improve wine composition.

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 score0.855
Threshold uncertainty score0.079

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 head0.140
GPT teacher head0.359
Teacher spread0.219 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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