Consumer Sensory Comparisons Among Beef, Horse, Elk, and Bison Using Preferred Attributes Elicitation and Check‐All‐That‐Apply Methods
Bibliographic record
Abstract
Despite their nutritional benefits, consumption of red meat from alternative sources such as bison, elk, and horse is low when compared to beef. Sensory attributes and drivers of liking were identified for these meats using the Preferred Attributes Elicitation (PAE) and Check-All-That-Apply (CATA) methods. For the PAE study, 25 panelists evaluated beef, horse, bison, and elk meats in three different group sessions (n = 7, 7, and 11), whereas 63 panelists participated in the CATA study. Consumers in both PAE and CATA studies associated horse meat with dry and fibrous appearance, whereas beef was associated with meaty/beefy flavor and aroma: bison with metallic and livery aroma and intense aftertaste and elk meat with livery, fishy, metallic flavor, musky aroma, and bloody aftertaste. Penalty analysis on the CATA data identified similar drivers of meat liking as the PAE groups. The attributes were juiciness, meaty/beefy aroma, tender texture, meaty/beefy flavor, and mild flavor and aroma. Attributes with significantly negative mean impact on liking were dryness, tough texture, livery flavor, and aftertaste. Association of these attributes with horse and elk meats has implication on drivers of dislike for these meat types. Cluster analysis identified a small group of consumers with preference for horse and elk meats, and this may present niche market opportunities for these meat types. Results showed that the PAE method was comparable to CATA for the evaluation of meat from different species and for identification of drivers of liking and that both methods are effective for meat sensory characterization. PRACTICAL APPLICATION: Lean red meat from unconventional sources such as elk, bison, and horse has unique sensory attributes that may influence acceptance. This study characterized the sensory attributes of these meats and their impact on liking using two rapid consumer descriptive profiling methods-PAE and CATA. Undesirable flavor and aftertaste attributes were identified as the major drivers of disliking for these unconventional meats. Both methods gave similar description of the samples, thus confirming the suitability of PAE for descriptive meat profiling by consumer panels.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".