Study of the Interaction of Anthocyanins with Phenolic Aldehydes in a Model Wine Solution
Bibliographic record
Abstract
Abstract Aldehydes may be present in wines as a result of metabolic processes during wine fermentation or through oxidation and extraction from wood during wine aging in oak barrels. Apart from acetaldehyde, the most abundant aldehyde in wine, other aldehydes such as furfural and more recently vanillin have shown to contribute to the formation of more stable pigments. The copigmentation effect of phenolic molecules, including flavanols and anthocyanins themselves, has been previously evaluated in wine and model solutions, and even the effect of aldehydes related to wine aging has been documented at different pHs and molar ratios. The copigmentation phenomenon is observed by hyperchromic effects and bathochromic shifts of λmax, and, in the same time, the presence of larger molecular weight pigments, potentially less susceptible to degradation, was followed up. This experimental work intended to evaluate the potential of five different aldehydes, all of which are safe for human consumption and are used in the food industry, to the formation of pyranoanthocyanin-like and polymeric pigments in the model solution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".