Effect of different drying methods combined with fermentation and enzymolysis on nutritional composition and flavor of chicken bone powder
Bibliographic record
Abstract
Chicken bone powder was dried by using spray drying (SD), hot air drying (AD), freeze drying (FD), and infrared freeze drying (IFD) in this study. Nutritional composition, antioxidant property, physical property, energy consumption, and flavor were compared, respectively. The results demonstrated that IFD powder had the highest content of amino acid nitrogen (49 mg/100 g). And in terms of polypeptide content, IFD and FD powder showed no significant difference, were obviously higher than AD and SD powder. As for antioxidant property, IFD and FD powder had higher DPPH and ·OH scavenging capacity than AD and SD powder. In addition, IFD powder had the highest L* value (74.59) and SD powder had the lowest L* value (65.35), which was consistence with the result of appearance score. Concerning flavor and sensory evaluation, IFD powder showed the highest umami value (11.85) and acceptability score (52.52) among four drying methods. Besides, the typical E-nose sensors response values of the IFD powder were closest to those of the enzymatic hydrolysate before drying and SD powder had the highest response value in all sensors. Finally on the basis of energy consumption, SD showed the minimum drying time and energy consumption.
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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 | 0.001 |
| 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) | 0.001 | 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".