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Record W2905081322 · doi:10.3390/molecules23123300

Evolution of Flavanol Glycosides during Red Grape Fermentation

2018· article· en· W2905081322 on OpenAlexfundno aff
Marie Zerbib, Guillaume Cazals, Marie‐Agnès Ducasse, Christine Enjalbal, Cédric Saucier

Bibliographic record

VenueMolecules · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersUniversité de MontpellierAlberta Water Research Institute
KeywordsWinemakingChemistryFermentationWineGlycosideFood scienceMalolactic fermentationEthanol fermentationMonomerFruit wineChromatographyBotanyOrganic chemistryBacteriaBiologyBrewing

Abstract

fetched live from OpenAlex

Monomeric and dimeric flavanol glycosides were quantified by UHPLC-MRM in Syrah (SYR) and Grenache (GRE) grapes and in their corresponding wines for the first time. Quantities were extremely variable depending on grape tissue (seeds or skins) and during fermentation. Overall, 22 monomeric and dimeric mono- and diglycosides were determined with concentrations ranging from 0.7 nanograms to 0.700 micrograms per gram of grape tissue, and 0 to 60 micrograms per liter for wines. The evolution of the glycosides' composition during winemaking suggests that almost all these compounds originate in the grapes themselves and display different extraction kinetics during winemaking. One isomer of the monomeric (epi) flavanol monoglycosides seemed to be biosynthesized by yeasts during wine fermentation. The sharp decrease in concentration of some isomers at the late stages of fermentation or after pressing suggests that some grape glycosidase activities convert these compounds into non-glycosylated flavanols.

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 score0.585
Threshold uncertainty score0.452

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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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