Using check‐all‐that‐apply to evaluate wine and food pairings: An investigation with white wines
Bibliographic record
Abstract
Abstract Growing consumer interest in food and wine pairing leads to a need for more studies to be conducted evaluating consumers' perception of food pairings. Many studies have used trained panelists to evaluate food and wine pairings; however, this study sought to determine how consumers evaluate food and wine pairings using the check‐all‐that‐apply (CATA) method. The participants (n = 112) were asked to evaluate five white wines for their liking of the wine, their sensory perception of the wines and identify which food items they would pair with the wine using a CATA question. The participants separated the wines based on their sweetness and dryness, as well as their acidity. The participants liked sweet, citrus, fruity and floral white wines, and disliked earthy and sour attributes. When the participants paired the wine with hard cheeses and chocolate their liking increased; however, when the wines were paired with French fries, steak, and lemon pie, it detracted from their liking. Future research should ask participants to explain their pairing choices using open‐ended comment questions. Practical Applications Very few studies have explored consumers' food and wine pairing preferences. This study identified which food consumers pair with different white wines and how food items can positively or negatively impact consumers' liking of the wine. The results of this study are important to those working in the food and wine service industry. The findings of this study contribute to the gap in knowledge of consumers' preferred food pairings and investigate the use of a check‐all‐that‐apply question to evaluate food and wine pairings. Future research should ask consumers to explain their food pairings, as well as evaluate how wine knowledge and familiarity with wine impact their pairing decisions.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".