How do consumers discuss the texture of frozen blueberries? An investigation using word association, hedonic scales and rate‐all‐that‐apply
Bibliographic record
Abstract
BACKGROUND: Flavour, texture, and extended shelf life are key quality traits for blueberries. Studies have used trained panelists and texture analysers to evaluate frozen blueberries. However, more studies are needed to investigate consumer perception and acceptance of frozen blueberries' texture. This study used word association, hedonic scales, and rate-all-that-apply to evaluate how consumers perceive the texture of frozen blueberries. RESULTS: Consumers were interested in the firmness of frozen blueberries, as well as crunchiness, softness, juiciness, and smoothness. They also identified the textural descriptors mushy, tough, chewy, squishy, and mealy. The participants separated the wild blueberries from the cultivated blueberries when evaluating their liking. Textural attributes were correlated with the consumers' overall liking (juicy, firm, crunchy, smooth positively and mushy, tough, squishy negatively). CONCLUSION: This study identified which textural attributes influence consumers' liking of frozen blueberries. Consumers preferred frozen blueberries that were firm, juicy and crunchy. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".