Formation of Hydrogen Sulfide in Wine: Interactions between Copper and Sulfur Dioxide
Bibliographic record
Abstract
The combined synergistic effects of copper (Cu(2+)) and sulfur dioxide (SO₂) on the formation of hydrogen sulfide (H₂S) in Verdelho and Shiraz wine samples post-bottling was studied over a 12-month period. The combined treatment of Cu(2+) and SO₂ significantly increased H₂S formation in Verdelho wines samples that were not previously treated with either Cu(2+) or SO₂. The formation of H₂S produced through Cu(2+) mediated reactions was likely either: (a) directly through the interaction of SO₂ with either Cu(2+) or H₂S; or (b) indirectly through the interaction of SO₂ with other wine matrix compounds. To gain better understanding of the mechanisms responsible for the significant increases in H₂S concentration in the Verdelho samples, the interaction between Cu(2+) and SO₂ was studied in a model wine matrix with and without the presence of a representative thiol quenching compound (4-methylbenzoquinone, 4MBQ). In these model studies, the importance of naturally occurring wine compounds and wine additives, such as quinones, SO₂, and metal ions, in modulating the formation of H₂S post-bottling was demonstrated. When present in equimolar concentrations a 1:1 ratio of H₂S- and SO₂-catechol adducts were produced. At wine relevant concentrations, however, only SO₂-adducts were produced, reinforcing that the competition reactions of sulfur nucleophiles, such as H₂S and SO₂, with wine matrix compounds play a critical role in modulating final H₂S concentrations in wines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".