Restoring Functionalities in Chicken Breast Fillets with Spaghetti Meat Myopathy by Using Dairy Proteins Gels
Bibliographic record
Abstract
The use of caseinate, whole milk powder, and two whey protein preparations (WP; 2% w/w) was studied in minced meat made with normal breast (NB), and ones showing spaghetti meat (SM). SM is an emerging myopathy known for muscle fiber separation and lower protein content, costing $100s of millions to the industry. Using SM without dairy proteins resulted in a higher cooking loss (SM: 3.75%, NB: 2.29%; p < 0.05), and lower hardness (SM: 29.83 N, NB: 34.98 N), and chewiness (SM: 1.29, NB: 1.56) compared to NB. Using dairy proteins, except WP concentrate and WP isolate, significantly improved yield and increased hardness. Adding WP isolate to SM resulted in a similar texture profile as NB samples without dairy proteins (34 and 35 N hardness; 0.22 and 0.24 springiness; 1.57 and 1.59 chewiness values, respectively). Adding caseinate and whole milk to SM showed a more substantial effect of improving water-holding capacity, increasing hardness, gumminess, and chewiness compared to adding WP; i.e., adding caseinate and milk powder resulted in higher values for those parameters compared to NB without additives. Overall, it is shown that dairy proteins can be added to SM to produce minced poultry meat products with similar or higher yield and texture profiles compared to using normal breast fillets.
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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) | 0.001 | 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".