Modifications of Phenolic Compounds, Biogenic Amines, and Volatile Compounds in Cabernet Gernishct Wine through Malolactic Fermentation by Lactobacillus plantarum and Oenococcus oeni
Bibliographic record
Abstract
Malolactic fermentation is a vital red wine-making process to enhance the sensory quality. The objective of this study is to elucidate the starter cultures’ role in modifying phenolic compounds, biogenic amines, and volatile compounds after red wine malolactic fermentation. We initiated the malolactic fermentation in Cabernet Gernishct wine by using two Oenococcus oeni and two Lactobacillus plantarum strains. Results showed that after malolactic fermentation, wines experienced a content decrease of total flavanols and total flavonols, accompanied by the accumulation of phenolic acids. The Lactobacillus plantarum strains, compared to Oenococcus oeni, exhibited a prevention against the accumulation of biogenic amines. The malolactic fermentation increased the total esters and modified the aromatic features compared to the unfermented wine. The Lactobacillus plantarum strains retained more aromas than the Oenococcus oeni strains did. Principal component analysis revealed that different strains could distinctly alter the wine characteristics being investigated in this study. These indicated that Lactobacillus plantarum could serve as a better alternative starter for conducting red wine malolactic fermentation.
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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) | 0.000 | 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".