Phosphorylation modification of collagen peptides from fish bone enhances their calcium-chelating and antioxidant activity
Bibliographic record
Abstract
In order to investigate the impact of phosphorylation modification on fish bone collagen peptide (CP), CP was subjected to phosphorylation modification to generate phosphorylated collagen peptide (P-CP). CP and P-CP were further chelated with calcium to form calcium-chelated collagen peptide (Ca-CP) and calcium-chelated phosphorylated collagen peptide (Ca-P-CP). The structural changes before and after phosphorylation and chelation reaction were characterized. Additionally, their stability and antioxidant activity were evaluated. The calcium-binding capacity of CP was significantly enhanced by phosphorylation, and the highest calcium-chelating capacity reached 128.21 mg/g. The introduction of phosphate ions caused esterification reaction, while the carboxyl groups, amino groups and phosphate groups of CP and P-CP were responsible for binding to calcium ions. Ca-P-CP exhibited an excellent stability (calcium retention rates >80%) towards a wide range of temperatures, pH as well as gastrointestinal digestion. Furthermore, DPPH and superoxide anion radical scavenging activities of CP and P-CP were significantly increased after chelation with calcium ions. Overall, phosphorylation modification can effectively improve the calcium-chelating ability of CP, while the resultant chelates possessed high antioxidant activity and stability. The chelates in the present study are promising to be applied as calcium supplements with high efficiency, bioactivity and stability.
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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".