Effects of spontaneous and inoculated fermentation on the total phenolic content and antioxidant activity of Cabernet Sauvignon wines and fermented pomace
Bibliographic record
Abstract
The total phenolic content and antioxidant activity of wine and fermented pomace (FP) from Cabernet Sauvignon grapes harvested at the stage of full ripeness were evaluated by spectrophotometric analysis. Wine and pomace were obtained after prolonged maceration during spontaneous and inoculated fermentation of fully ripe grapes. Three individual vinifications were inoculated with the following commercial yeasts: BDX (Lallemand, Montréal, QC, Canada), FX10 (Laffort, Bordeaux, France) and Qa23 (Lallemand, Montréal, QC, Canada). For each vinification, maceration lasted 0, 3, 5, 7, 14 and 21 days, respectively. The total phenolic content was determined spectrophotometrically using the Folin-Ciocalteu method. Two different methods were used to evaluate the antioxidant activity of the wine and pomace samples: the Ferric Reducing Activity of Plasma (FRAP) and the Trolox Equivalent Antioxidant Capacity (TEAC). The use of a winemaking process that included different maceration times and inoculation with yeasts, as well as spontaneous fermentation, significantly modulated the total phenolic content of the obtained wines and FP. This study could provide a good basis for the practical application and obtaining wines with a high content of phenolic compounds and antioxidant properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".