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Transient acetaldehyde production by SO2-producing <i>Saccharomyces cerevisiae</i> promotes the survival of <i>Oenococcus oeni</i> during co-fermentation

2023· article· en· W4380990540 on OpenAlexfundno aff
Peter J. Costello, Radka Kolouchova, Jane McCarthy, Damian Espinase Nandorfy, Desireé Likos, Simon A. Schmidt

Bibliographic record

VenueOENO One · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersPernod RicardWine AustraliaAustralian GovernmentAlberta Water Research Institute
KeywordsOenococcus oeniMalolactic fermentationFermentationYeastFood scienceAcetaldehydeWineSaccharomyces cerevisiaeEthanol fermentationDiacetylChemistryBiologyBacteriaBiochemistryEthanolLactic acid

Abstract

fetched live from OpenAlex

Stuck or sluggish malolactic fermentation (MLF) can be problematic in stressful wine conditions, particularly white and sparkling base musts/wines. In these cases, knowledge of yeast-bacteria strain compatibility and the amount of sulfur dioxide (SO2) a yeast strain produces are important considerations for successful MLF. Here, laboratory- and pilot-scale co-fermentations in Chardonnay were used to investigate the effect of yeast-derived SO2 on Oenococcus oeni survival. Although yeast-derived SO2 is generally inhibitory, we show that SO2 production (to approximately 65 mg/L) can be uncoupled from O. oeni survival in the early stages of co-fermentation. Bacterial survival in the presence of specific SO2-producing yeast strains was correlated with the early, transient formation of high acetaldehyde concentrations. Oenococcus oeni survival coincided with molecular SO2 concentrations remaining below an extremely low threshold of inhibition, which exponentially increased from approximately 3–6 µg/L in the first three days of co-fermentation. Strain-dependent sensitivity of O. oeni to bound SO2 remains a possibility, although the extent and mechanism of such inhibition by the SO2 adduct during co-fermentation remain unclear. The choice of co-inoculation yeast strain also influenced wine diacetyl concentration, which was only detected in wines co-inoculated with high SO2-producing S. cerevisiae strains. The wines with high diacetyl concentrations were found to be distinct by a sensory panel, with comparatively high citation frequency for a buttery sensory attribute. Both the SO2- and acetaldehyde production capacity of yeasts are, therefore, seen as meaningful co-inoculation selection criteria. The range of yeast strains suitable for MLF induction by co-inoculation could be widened to include SO2-producing strains that transiently produce an early, high concentration of acetaldehyde. The effects of low, equilibrium concentrations of molecular SO2 should also be considered in conjunction with total SO2 as a measure of SO2 toxicity towards O. oeni following co-inoculation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.027
GPT teacher head0.242
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2023
Admission routes1
Has abstractyes

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