Consumers’ familiarity with wine increased their acceptance of pasta sauce containing grape pomace
Bibliographic record
Abstract
Grape pomace (GP) is a waste product of the winemaking process and has been proposed as a nutritionally beneficial ingredient, as it contains phenolic compounds, dietary fiber, and antioxidant activity. It can be a polarizing ingredient due to its flavor components. Familiarity has been found to influence consumers' preferences and sensory perception of food. A sensory test was conducted to evaluate the acceptance, sensory perception, and emotional response to pasta sauces containing GP (3% [3GP], 6% [6GP], 9% [9GP] by volume and control without GP addition). The sensory trials included wine consumers (n = 44) and nonconsumers of wine (n = 58) to determine how consumers' familiarity with the flavor properties of GP influenced their perception of the pasta sauce. Overall, the addition of GP decreased the liking scores of the GP-containing sauces, but the wine consumers' hedonic scores for the control, 3GP, and 9GP were significantly higher than the nonconsumers. Both consumer groups identified that the samples with a higher amount of GP addition were associated with sour, bitter, astringency, grainy, and gritty attributes. However, the wine consumers used more positive emotions to describe their emotional response to the GP-containing samples. The study identified that GP led to off-flavors and textures in the pasta sauces. PRACTICAL APPLICATION: GP is currently a waste product, but it has many nutritional benefits. Consumers are increasingly looking for nutritional benefits from their food. When incorporated into pasta sauces, GP decreased the acceptance of the pasta sauce and negatively impacted the flavor and texture. Familiarity has been found to impact consumer acceptance, and wine consumers had a more positive emotional response and higher hedonic scores in response to the GP-containing pasta sauce than nonconsumers of wine.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".