Relationships between wine phenolic composition and wine sensory properties for Cabernet Sauvignon (<i>Vitis vinifera</i>L.)
Bibliographic record
Abstract
Background and Aims: Winemakers from a commercial winery observed sensory differences in Cabernet Sauvignon wines made from three pruning treatments in a single vineyard, particularly in mouthfeel characteristics. This study examined the relationships between wine composition and wine sensory characteristics, then related these to berry weight and composition and wine quality scores. Methods and Results: Cabernet Sauvignon from three pruning treatments – Machine, Cane and Spur – was harvested at commercial harvest date, and replicate wines were made from each for three vintages. The composition of the wines from all three pruning systems was generally similar. Differences in individual descriptive attributes did not separate the wines from the three treatments, or across vintages, despite differences in overall quality scores. Principal component analysis (PCA) could separate the wines by pruning and by vintage using wine composition or sensory parameters. Higher concentrations of anthocyanins, tannins and phenolics in berries did not always result in higher concentrations in wines. Conclusions: In this study, higher wine tannin or wine phenolic concentrations did not result in higher wine astringency, and wine colour measures and phenolic composition were not good indicators of individual wine sensory properties or wine quality. Wine composition was not necessarily directly influenced by berry composition. Significance of the Study: Few studies focus on the berry to wine to sensory continuum, particularly over more than one vintage or in a commercial context. This study highlighted how complex the relationships among berries, wine sensory properties and wine quality can be, particularly within a single vineyard.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".