Preparation and physicochemical characterization of films prepared with salmon skin gelatin extracted by a trypsin-aided process
Bibliographic record
Abstract
The recovery of gelatins from Atlantic salmon (Salmo salar) skin for film formation and characterization was studied. Fish skins pre-treated with trypsin (250 U/g) produced the highest hydroxyproline content (7.41 ± 0.49 mg hydroxyproline/g treated skin) and yield (53.05 ± 4.38%) of gelatin, as compared to the use of saline solution. Pre-treatment with a lower concentration of trypsin (1 U/g) at a shorter pre-treatment time successfully reduced the degradation of gelatin with co-production of high molecular weight α-chains. Gelatin was further extracted by a trypsin-aided process for film formation and characterization. Films with increasing protein concentration (from 1 to 5%, w/v) exhibited higher thickness, tensile strength, and elongation at break (EAB), but a marked decrease in EAB for films with 6 and 7% (w/v). Films with 5% proteins showed higher thickness, lower tensile strength and higher EAB with increasing concentrations of glycerol (from 10 to 50% of proteins, w/w). All films exhibited high water uptake, decrease in light transmission and an increase in opacity as the protein and glycerol contents increased. Electrophoretic studies showed that the increase in the mechanical properties of the films was correlated with the increase in protein concentration, owing to the increased content of high molecular weight chain fractions. Furthermore, Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) revealed the interaction between the proteins and glycerol for all films. This study demonstrated the viability of the trypsin supplementation process to obtain salmon skin gelatin for film formation.
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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) | 0.001 | 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".