Bovine biceps femoris is resistant to tenderization by lower-salt moisture enhancement or enzyme addition
Bibliographic record
Abstract
Unacceptable toughness in retail beef cuts prepared from round muscles is not uncommon. The biceps femoris (BF) muscle is a particular challenge due to its high connective tissue content. Disruption of connective tissue by proteolytic enzyme injection has been demonstrated to improve tenderness in some muscles. Moisture enhancement can also be effective; however, concern over sodium content in processed foods is rising. The single and combined effects of lower-salt moisture enhancement (ME; 0.25% sodium chloride/0.25% sodium phosphate) and injection of enzymes from different sources (fungal aspartyl protease, bacterial protease, porcine pancreatin, plant-derived papain) on tenderness characteristics of the BF was examined. The enzyme and ME treatments were not interactive. Moisture enhancement had no impact on peak shear force or sensory tenderness, although juiciness and saltiness perception was enhanced, even at the relatively low salt level. Each enzyme treatment reduced the shear force associated with the myofibrillar component of the BF, but did not influence the connective tissue component. No off flavour development or other sensory defects resulted from enzyme treatment. The ineffectiveness of the treatments may have been due to the low salt level, lack of enzyme specificity for collagen, or the relatively intractable nature of the BF to tenderization treatments.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".