The effects of enzyme and phosphate injections on the quality of beef semitendinosus
Bibliographic record
Abstract
In light of palatability concerns with beef semitendinosus (ST), its resistance to tenderization by more conventional methods, and its large content of elastin, investigation into the efficacy of enzyme injection was undertaken. Pancreatin, an enzyme cocktail for cheese making, was chosen for its content of elastase, amongst other enzymes, making it a suitable choice for use in the ST. To compare with uninjected controls, samples were prepared to contain a conservative level (0.01% delivered) of pancreatin, salt/phosphate brine, or a combination of enzyme with brine, injected to 105 or 110% green weight. Injection treatment significantly affected several meat quality attributes, while injection level did not. Where water alone was used as the enzyme carrier for injection, an excessive amount of drip loss was observed, with no improvement in tenderness. Salt/phosphate improved brine retention, increased pH, and resulted in darker meat colour. A significant reduction in shear was observed following injection with the enzyme/brine combination, results indicating that ST tenderness can be improved by 9–15% with the modest level of pancreatin used in this injection treatment. Further investigation is recommended to evaluate the tenderization potential using higher enzyme levels, and to examine the safety and sensory aspects of pancreatin use. Key words: Enzyme, moisture enhancement, phosphate injection, elastase, beef semitendinosus
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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) | 0.001 | 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".