Low molecular weight bioactive peptides derived from the enzymatic hydrolysis of collagen after isoelectric solubilization/precipitation process of turkey by-products
Bibliographic record
Abstract
A process based on the isoelectric solubilization/precipitation (ISP) method was developed to recover collagen from low value poultry by-products. The application of the ISP process to turkey heads generated protein isolates and an insoluble biomass that was used to extract collagen. Isolated turkey head collagen was then enzymatically hydrolyzed for different time periods using alcalase, flavorzyme, and trypsin. The enzymatic hydrolysis approaches consisted of digesting collagen with each one of the 3 enzymes alone (alcalase, flavorzyme, or trypsin), or one of the 3 combinations of 2 enzymes (alcalase/flavorzyme, alcalase/trypsin, or flavorzyme/trypsin), or a cocktail of all 3 enzymes together (alcalase/flavorzyme/trypsin). The molecular weight distribution of turkey head collagen hydrolysates was determined using size exclusion chromatography and matrix-assisted laser desorption ionization-time of flight-mass spectrometry. The enzyme cocktail produced collagen hydrolysates with the greatest amount of low molecular weight peptides ranging from 555.26 to 2,093.74 Da. These collagen peptides showed excellent solubility over a wide pH range (2 -: 8) and were able to bind cholic and deoxycholic acids and significantly (P < 0.05) inhibited plasma amine oxidase in a dose- and time-dependent manner. The ISP process combined with enzyme cocktail hydrolysis represents a potential new way to produce low molecular weight bioactive collagen peptides from low value poultry by-products.
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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) | 0.001 | 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) | 0.000 | 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 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".