Toward the high-efficient utilization of poultry blood: Insights into functionality, bioactivity and functional components
Bibliographic record
Abstract
A large amount of poultry blood is annually generated, and currently underutilized or largely disposed of as waste, resulting in environmental pollution and waste of protein resources. As one of the main by-products during the poultry slaughter process, the produced poultry blood can serve as a promising food ingredient due to its excellent functional properties and abundant source of essential amino acids, bioactive peptides and functional components. This work provides a comprehensive summary of recent research progress in the composition, functional and bioactive properties, as well as the functional components of poultry blood. Furthermore, the main preparation methods of poultry blood-derived peptides and their bioactivities were reviewed. In addition, their potential applications in the food industry were discussed. Overall, poultry blood is characterized by excellent functionalities, including solubility, gelation, foaming, and emulsifying properties. The major preparation methods for poultry blood-derived peptides include enzymatic hydrolysis, ultrasound-assisted enzymatic methods, macroporous adsorbent resins, and subcritical water hydrolysis. Poultry blood-derived peptides exhibit diverse bioactivities. Their metallic off-flavors and bitterness can be improved by exopeptidase treatment, Maillard reaction, and plastein reaction. In addition, poultry blood is also abundant in functional components such as hemoglobin, superoxide dismutase, immunoglobulin, and thrombin.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".