Variations in the effect of malolactic fermentation on the chemical and sensory properties of Cabernet Sauvignon wine: interactive influences of<i>Oenococcus oeni</i>strain and wine matrix composition
Bibliographic record
Abstract
Background and Aims: This study investigated the interactive effects of malolactic bacterial strain, pre-malolactic fermentation (MLF) pH value and wine matrix/style on modulating chemical and sensory impacts of MLF in Cabernet Sauvignon wine. Methods and Results: Malolactic fermentation was conducted in two styles of Cabernet Sauvignon wine, a lighter, fruity style and a more complex style. Each wine was divided into two equal volumes, one was adjusted to pH 3.3 the other to pH 3.7. Each of these wines was further divided into four equal volumes, three of which were inoculated with three different commercial Oenococcus oeni strains and the fourth used as a non-MLF control. Following MLF, all wines were standardised to approximately pH 3.5. The MLF treatments exhibited significant strain- and wine matrix-dependent effects on a diversity of chemical components, including esters, volatile acids and higher alcohols, and on colour. Descriptive sensory analysis also demonstrated significant effects on several sensory properties. Partial least squares analysis revealed a strong correlation between important chemical components and sensory attributes, including overall fruit flavour and dark fruit aroma. Conclusions: The extent and diversity of the impacts of MLF on wine chemical and sensory properties were directly influenced by choice of bacterial strain, pre-MLF pH and wine matrix composition. Relatively harsh conditions (pH 3.3, 14.8% alcohol) protracted the time of MLF and supported greater differences between Oenococcus oeni strains in their modulation of important wine chemical components. Significance of the Study: This study increases knowledge of the significant impacts of MLF on wine chemical composition and associated sensory properties. Specifically, variations in the effect of MLF on wine sensory and chemical properties has been found to arise from the choice of bacterial strain and wine matrix composition/style.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".