Plant extracts effectiveness to extend bison meat shelf life
Bibliographic record
Abstract
The effectiveness of plant extracts (0.05% rosemary and 0.08% oregano) to extend shelf life of bison strip loin steaks in terms of color stability and consumer acceptability was studied. Steaks treated with oregano presented lower oxygen consumption, higher metmyoglobin-reducing activity (MRA), decreased lipid oxidation, and provided a stable red color with less discoloration during the retail display period than the control and rosemary treated steaks (P < 0.05). Results from consumer sensory evaluation indicated that treated steaks under study were not significantly different from the control (P > 0.05) based on palatability attributes and acceptability. However, rosemary treated steaks were more desirable and palatable than their oregano counterparts (P < 0.05). Overall, plant extracts, particularly oregano, can improve color stability of bison steaks due to its antioxidants properties and ability to increase MRA capacity in fresh bison meat without posing any negative impact on its sensory attributes. PRACTICAL APPLICATION: This study will provide valuable information to the bison meat industry on how to offer a more consistent and acceptable product (in terms of palatability and color) to consumers using plant-based natural antioxidants, without diminishing the palatability of their products. This technology can offer two more days of shelf life in retail overwrap packaging, consequently, opening the possibility for the bison industry to expand its market with a potential to reduce retail losses due to poor color stability and early browning (that is, stock out, markdowns, and waste due to expired display life).
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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 | 0.000 | 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 | 0.000 |
| 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".