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Record W3002120261 · doi:10.3390/molecules25030472

Impact of Botrytis cinerea Contamination on the Characteristics and Foamability of Yeast Macromolecules Released during the Alcoholic Fermentation of a Model Grape Juice

2020· article· en· W3002120261 on OpenAlexaff
Richard Marchal, Thomas Salmon, Ramón González, Belinda Kemp, Céline Vrigneau, Pascale Williams, Thierry Doco

Bibliographic record

VenueMolecules · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsBrock University
Fundersnot available
KeywordsBotrytis cinereaBotrytisYeastChemistryWineFermentationEthanol fermentationFood scienceMalolactic fermentationGel electrophoresisBiochemistryBiologyBotanyBacteriaLactic acid

Abstract

fetched live from OpenAlex

Botrytis cinerea is a fungal pathogen responsible for the decrease in foamability of sparkling wines. The proteolysis of must proteins originating from botrytized grapes is well known, but far less information is available concerning the effect of grape juice contamination by Botrytis. The impact from Botrytis on the biochemical and physico-chemical characteristics of proteins released from Saccharomyces during alcoholic fermentation remains elusive. To address this lack of knowledge, a model grape juice was inoculated with three enological yeasts with or without the Botrytis culture supernatant. Size exclusion chromatography coupled to multi-angle light scattering (SEC-MALLS) and sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) techniques (AgNO3 and periodic acid Schiff staining) was used in the study. When Botrytis enzymes were present, a significant degradation of the higher and medium MW molecules released by Saccharomyces was observed during alcoholic fermentation whilst the lower MW fraction increased. For the three yeast strains studied, the results clearly showed a strong decrease in the wine foamability when synthetic musts were inoculated with 5% (v/v) of Botrytis culture due to fungus proteases.

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 score0.907
Threshold uncertainty score0.106

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 head0.029
GPT teacher head0.248
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

Citations8
Published2020
Admission routes1
Has abstractyes

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