How do consumers discuss the texture of frozen blueberries? An investigation using word association, hedonic scales and rate‐all‐that‐apply
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it