MétaCan
Menu
Back to cohort

Tannin additions decrease the concentration of malodorous volatile sulfur compounds in wine-like model solutions and wine

2025· article· en· W4406127542 on OpenAlexfundno aff
Marlize Z. Bekker, Allie C. Kulcsar, Alicia Jouin, V. Felipe Laurie

Bibliographic record

VenueFood Chemistry · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersDepartment of Education and TrainingWine AustraliaAustralian GovernmentAlberta Water Research Institute
KeywordsWineTanninChemistrySulfurAging of wineOrganic chemistryFood scienceChromatography

Abstract

fetched live from OpenAlex

Hydrogen sulfide (H 2 S), methanethiol (MeSH) and ethanethiol (EtSH) are volatile sulfur compounds (VSCs) produced during winemaking and are associated with negative ‘reductive’ aromas in wine. Anecdotal evidence suggests that oenological tannins may be used to remediate the ‘reductive’ character of wines, yet little scientific evidence or explanation supporting this observation has been published. In this study, it was found that the addition of oenological tannin significantly decreased H 2 S, MeSH, and EtSH in model wine by 92 %, 90 % and 86 %, respectively. Furthermore, the removal of H 2 S, MeSH, and EtSH from model wine matrices was accelerated at high pH levels. The reaction products of polyphenol oxidation and their ability to ameliorate the presence of MeSH and EtSH were studied in model systems and wines, and vescalagin/castalagin adducts of MeSH and EtSH were subsequently produced. This study provides evidence for the mechanism through which oenological tannins may diminish ‘reductive’ aromas in wine. • Tannin significantly decreased H 2 S and MeSH in model and real wine. • Vescalagin/castalagin, and gallic acid VSC-adducts were produced. • Higher pH promoted the removal of H 2 S, MeSH, and EtSH. • A mechanism is proposed by which oenological tannins decrease ‘reductive’ aromas.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.252

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 head0.019
GPT teacher head0.218
Teacher spread0.199 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueFood ChemistrySame topicFermentation and Sensory AnalysisFrench-language works237,207