Consumer Sensory Acceptance of Standard Pre-cooked Hamburger Patties versus Premium Patties
Bibliographic record
Abstract
Consumers’ increasing concerns toward nutrition, health, and sustainable food have influence food industry. Practitioners in the meat product industry and retailers are focusing on premium labeled meat products, such as Certified Angus Beef and grass-fed beef, to meet consumers’ demand. Although many consumers assume the premium has better taste and texture, there is little research comparing the sensory attributes of the premium and non-premium burgers. This study compared the sensory attributes of three different hamburger patties: flame broiled pre-cooked beef (non-premium, standard patties), Angus beef, and grass-fed beef patties (premium patties). The results show that participants prefer pre-cooked hamburger patties significantly than Angus and grass-fed patties in initial taste and flavor. Also, this pre-cooked hamburger patties are significantly preferred compared to grass-fed patties in overall quality and overall liking attributes. Other sensory attributes, such as appearance, texture, juiciness, and seasoning, show no significant difference among three different patties. This indicates that the pre-cooked hamburger patties can be preferred than (or compatible to) Angus or grass-fed patties.
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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 | 0.001 |
| 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) | 0.003 | 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".