<i>Agaricus bisporus</i> chitosan influences the concentrations of caftaric acid and furan-derived compounds in Pinot noir juice and base wine
Bibliographic record
Abstract
Chitosan is a fining agent used in winemaking, although its use in juice and wine beyond fining has been limited until now. Therefore, this study's first aim was to determine if chitosan derived from Agaricus bisporus (button mushrooms) could reduce caffeic and caftaric acid concentrations in Pinot noir grape juice (Study A). The second aim was to determine if chitosan, when added to base wine, could influence the synthesis of furan-derived compounds during storage (Study B). In Study A, Pinot noir grape juice was stored at 10 °C for 18 hours after the following treatments: control (no addition), bentonite/activated charcoal (BAC), low molecular weight (< 3 kDa; LMW) chitosan, med. MW (250 kDa; MMW) chitosan, and high MW (422 kDa; HMW) chitosan (all 1 g/L additions). Caftaric acid was decreased, and total amino acid concentration was increased in the LMW chitosan-treated juice, while the estimated total hydroxycinnamic acid content, turbidity, and browning were decreased in the MMW chitosan-treated juice compared to the control. In Study B, Pinot noir base wine destined for sparkling wine was stored at 15 and 30 °C for 90 days with the following treatments: control (no addition), LMW chitosan, MMW chitosan, and HMW chitosan (all 1 g/L additions). The three chitosan treatments stored at 30 °C had increased furfural, homofuraneol, and 5-methylfurfural formation in the base wine compared to the control. At 15 °C, furfural and homofuraneol had greater concentrations in all chitosan-treated wines after 90 days of storage. Our results demonstrate the potential of mushroom-derived chitosan to remove caftaric acid from grape juice and suggest that chitosan can influence the synthesis of furan-derived compounds in wine after short-term storage.
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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) | 0.000 | 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".