Extraction of individual phenolic acids during vinification of Cabernet Sauvignon grape variety
Bibliographic record
Abstract
During winemaking, different classes of phenolic compounds are extracted from the solid parts of the grapes and processed into wine. Various factors can affect the extraction and final content in the wine, e.g. grape variety, temperature, vinification technique, maceration, yeast and enzyme addition, etc. In this study, the focus was on certain phenolic acids (caffeic acid, vanillic acid, phydroxybenzoic acid, protocatechuic acid and ellagic acid), which are among the most important non-flavonoids of grape berries. The wine was obtained after prolonged maceration during the spontaneous and inoculated fermentation of Cabernet grapes. Two fermentations were carried out, one spontaneous and one inoculated, and the maceration lasted 0, 3, 5, 7, 14 and 21 days respectively. The grape must be inoculated with the yeast strain Saccharomyces cerevisiae (BDX, Lallemand, Canada) and K2S2O5 (10 g per 100 kg) was added in both vinifications. The wine samples were prepared for LC-MS/MS analysis by solid phase extraction. An exponential increase was observed for all phenolic acids measured. Among these compounds, phydroxybenzoic acid required only 3 days for its maximum extraction (1.0485 mg/l) during BDX vinification. In contrast, spontaneous vinification required 8 days of maceration for maximum extraction. The highest extraction value for caffeic acid was achieved on the 12th day of maceration at 5.831 mg/l in spontaneous fermentation. Inoculated vinification contributed to higher extraction values for vanillic and protocatechuic acid than spontaneous vinification. For ellagic acid, maximum extraction was observed at the end of the maceration period (day 21). This study offers a perspective for future research and practical application in the wine industry with the aim of obtaining wine with high phenolic content.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".