Enhancing the sensory properties and consumer acceptance of warm climate red wine through blending
Bibliographic record
Abstract
Proline has recently been found to direct several sensory attributes in red wine, including viscosity, fruit flavour and sweetness. We sought to investigate whether a red wine, deemed ‘flavour deficient’ by a producer, from a warm inland region could be improved by blending with a high proline wine from the same region, compared to a high colour and flavour wine, linking consumer acceptance with sensory properties and chemical composition. Three dry red wines (two Cabernet-Sauvignon wines from a warm region and one Lagrein wine from a cooler region) were blended in a constrained mixture design. Several blends were uncovered with improved sensory properties and consumer liking scores. Increased liking scores were related to heightened perceived Viscosity (unrelated to physical viscosity), Sweetness and Berry flavours, connected to proline-rich wines with small proportions of Lagrein. PLS-R models relating blend chemical composition, sensory properties and consumer acceptance associated Astringency and Bitterness to polyphenolics and organic acids and lower liking scores. Vegetal and Leather aromas in blends also reduced consumer acceptance and were related to the concentration of the thiols 3SH, 3SHA, PMT, 2FMT and MeSH, as well as guaiacol and isobutyl methoxypyrazine. Multiple blends successfully improved consumer acceptance of the ‘flavour deficient’ wine, particularly those with an increased proportion of the proline-rich wine. Non-linear effects resulting from blending were also assessed, with most variables modelled best by linear averaging. This study demonstrates the practical application of a design of experiment approach using sensory properties, proline and polyphenolic concentrations to guide wine blending and improve wine flavour and acceptability.
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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) | 0.001 | 0.001 |
| 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".