ADDING GRAPE SEED EXTRACT TO WINE AFFECTS ASTRINGENCY AND OTHER SENSORY ATTRIBUTES
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT This research note explored the sensory and analytical effects of adding grape seed extract (GSE; 0.0, 0.5, 1.0, 2.5 and 5.0 g/L) to a commercial red wine. Total phenol, color intensity and hue analyses were conducted. Sensory profiling, using 12 trained judges, evaluated the intensity of astringency, fruity and woody/earthy aromas, and red color of the wines. Special care was taken to avoid perceptual biases among the sensory attributes, by conducting the astringent, aromatic and color determinations independently of one another. Analyses of variance were used to evaluate the sensory effects, while regression analyses were used to relate the mean sensory attributes to the GSE concentrations. Positive linear regressions were observed between GSE and astringency ( R 2 = 0.841), woody/earthy aroma ( R 2 = 0.933) and color ( R 2 = 0.925), while a negative linear regression was observed for fruity aroma ( R 2 = 0.911). The presence of GSE significantly enhanced the woody/earthy aroma and suppressed the fruity aroma. PRACTICAL APPLICATIONS This research note demonstrated that GSE not only influenced the mouthfeel of a wine, but also the color and aroma. Because the perceived sensory attributes (astringency, color, fruity and woody/earthy) are highly correlated [|0.801| ≤ R ≤ |0.982|] and dependent on the type of wine and GSE, winemakers are advised to conduct in‐house trials prior to tannin adjustments in the cellar. As demonstrated in this research note, the sensory changes can be successfully modeled using linear regression to allow winemakers to predict the change in aroma, color and astringent attributes, associated with the addition of GSE.
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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 | 0.000 | 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 it