Hydrolyzed collagen from porcine lipase‐defatted seabass skin: Antioxidant, fibroblast cell proliferation, and collagen production activities
Bibliographic record
Abstract
Defatting of seabass skins using porcine pancreas lipase (PPL) at 25 or 50 units/g dry matter) for 1-3 hr at 30ºC was investigated. Treatment of seabass skin with PPL (25 unit/g dry matter) for 3 hr removed 83.81% lipids when compared to 57.27% using isopropanol. Hydrolysis of PPL-treated skin by papain (0.3 unit/g dry matter) (PPL-papain-3 process) at 40ºC for 90 min provided hydrolyzed collagen (HC) with higher yield, α-amino group content, ferric-reducing antioxidant power, and metal chelating activity than other treatments (p < 0.05). There was no difference in fishy odor between HC from PPL-papain-2 and PPL-papain-3 processes (p > 0.05). All the HC (50-250 µg/ml) samples stimulated L929 fibroblast cell proliferation and also induced collagen production in a dose-dependent manner. Also, all HC contained peptides with molecular weight of 406-11,860 Da. Gly and imino acids were dominant amino acids in HC prepared with PPL-papain-3 process. PRACTICAL APPLICATIONS: Seabass skin is a potential raw material for the production of hydrolyzed collagen (HC). However, seabass skin contains a large amount of lipids, including polyunsaturated fatty acids. These unsaturated lipids are oxidized during processing, particularly during hydrolysis at high temperature. This leads to the development of undesirable odor, especially fishy odor. Therefore, seabass skin defatting is an important step for improving the quality of the resulting HC. The use of lipase is an alternative method that can be used to remove lipids in skins without using solvents. HC from defatted skins will contain bioactive peptides and therefore, can be used as a food supplement or for skin nourishment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".