Predicting protein haze formation in white wines
Bibliographic record
Abstract
Background and Aims Wine protein haze formation is commonly predicted by a heat test; however, the conditions used in the test can vary widely between laboratories. Here, we investigate the influence of heating and cooling conditions on heat test results. Methods and Results White wines were heated at 80°C for a time that varied from 0.5 to 6.0 h and then cooled for 0.5–18 h at either 0, 4 or 20°C. The turbidity was measured before heating and after cooling. Longer heating times, longer cooling times and a lower cooling temperature all increased the amount of haze produced in the heat test. Bentonite fining trials with eight white wines indicated that after 2 h heating, cooling either at 4°C for 18 h, 0°C for 3 h or 20°C for 3 h had no impact on the predicted dose. Heating for 6 h with 18 h cooling at 4°C (24 h test) generally increased the predicted bentonite dose by up to 0.3 g/L compared with heating for 2 h. Wines fined at the bentonite dose recommended by a 5 h test or a 24 h test were generally clear after storage at 17 or 28°C for 12 months while the unfined Control wines generally became hazy. Conclusions Wines heated for 2 h at 80°C and subsequently cooled for 3 h at 20°C (5 h heat test) enabled the repeatable production of haze and bentonite fining dose. Significance of the Study Heat tests used in the wine industry need to include consistent heating and cooling conditions for reliable results and can be achieved in less time than previously recommended.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".