Assessing desirable levels of sensory properties in Sauvignon Blanc wines - consumer preferences and contribution of key aroma compounds
Bibliographic record
Abstract
Background and Aims: This study aimed to investigate the sensory properties and aroma compounds responsible for driving consumer preference of Sauvignon Blanc wines. Methods and Results: Three thiols, including their respective enantiomers, a methoxypyrazine and a combination of esters were added singly and in combinations to a neutral white wine at realistic concentrations to mimic Sauvignon Blanc wines. A sensory descriptive analysis of 21 samples was conducted. While each thiol contributed to tropical and cat urine/sweaty attributes, 3-mercaptohexyl acetate (3MHA) was of particular importance. The ‘green’ characteristics were primarily related to methoxypyrazine, but thiols were also found to contribute to a cooked green vegetal attribute. The aroma and flavour of the methoxypyrazine dominated the sensory properties of other components. The thiol S-enantiomers gave higher cooked green vegetal or cat urine/sweaty than their R-counterparts. Six combinations and the base wine were evaluated for liking by 150 consumers. One cluster of consumers (31%) preferred wines with higher tropical and confectionary aroma; a second cluster (43%) preferred wines with ‘green’ attributes; and the final cluster's liking was positively related to solvent and ‘green’ attributes, and negatively to tropical and cat urine/sweaty. Conclusions: There were strong and varied interactive effects among the compounds studied. A sizeable proportion of consumers tested preferred the samples with ‘green’ attributes, with a minority preferring the ‘fruit’ aromas. Significance of the Study: This study has shown clear definition of sensory attributes resulting from aroma compounds important to Sauvignon Blanc wines, their interactions and their effects on consumer preference.
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) | 0.002 | 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".