Investigating the disclosure of ingredient lists impact on consumers' sensory perceptions of red wines produced in Nova Scotia, Canada
Bibliographic record
Abstract
Abstract Wineries have started to state their ingredients list on their bottles of wine; however, they have not considered how this may affect consumers' acceptability and sensory perception. The first objective was to determine the attributes consumers used to describe Nova Scotia (NS) red wines. The second objective was to identify the impact ingredient lists have on consumers' sensory perceptions of wine. In the first trial, 81 participants evaluated NS red wines (n = 8) using projective mapping and ultra‐flash profiling. In the second trial, three red wines were selected to be evaluated with and without an ingredient list. Participants (n = 98) evaluated the wines through a check‐all‐that‐apply questionnaire and 9‐point hedonic scales. Sweet, fruity, and floral attributes were used more frequently to describe the wines with a shorter ingredient list. However, there was no significant difference in the acceptability of wines when they were evaluated blinded and with the ingredient list. Practical applications Consumers are asking for more information on to be presented on alcoholic beverages, including ingredients. Understanding which attributes contribute to and the impact of ingredient lists on consumer liking is vital to the wine industry. This study used check‐all‐that‐apply and hedonic scales were used to evaluate how the presence of ingredient lists affected the participants' sensory perception and overall liking of the red wines. It was determined that the presence of ingredient lists did not significantly affect the acceptability or the perceived sensory properties of red wines.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".