Consumers’ Perceptions and Sensory Properties of Beef Patty Analogues
Bibliographic record
Abstract
The present study was carried out to gain consumer insights on the use of tempeh (a fermented soy bean product) to improve the healthiness of beef patties and to determine the acceptable level of tempeh (10%, 20%, or 30%) in the patty. The study consisted of conducting two focus groups (n = 15), a pilot sensory evaluation, and a full consumer sensory study. The focus groups were asked about their consumption of beef patties, attitudes towards processed meat, attitudes towards negative aspects of red meat consumption, and attitudes towards tempeh consumption, as well as sensory perceptions of the cooked patties and their visual acceptance of raw patties. Focus group discussions suggested that there was a market for the product if consumers were informed of tempeh health benefits. Participants seemed more willing to choose how to balance their diet with an antioxidant source than buy a beef patty with added antioxidants. The focus group participants rated the visual attributes of raw patties from all treatments and it was found that the 20% tempeh and 30% tempeh patties were ranked lower (p < 0.05) than the others. Overall, the sensory experiments showed that the inclusion of 10% tempeh was the most acceptable level of addition. There were no significant (p > 0.05) differences between the control and 10% tempeh patties for overall acceptability or acceptance of flavor. However, 10% tempeh patties were found to be more tender and juicier than the control (p < 0.05). A proper knowledge and awareness of consumers about the benefits of tempeh could allow the development of beef containing tempeh products.
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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 | 0.001 | 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".