Extraction and Characterization of Gelatin from Bovine Lung
Why this work is in the frame
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Bibliographic record
Abstract
Gelatin is extracted from animal tissues using heat usually with low yields, but pepsin may increase high quality gelatin yield per unit of tissue. Gelatin from bovine lungs was extracted using heat and pepsin and the resulting gelatins were characterized. Pepsin increased gelatin yield by about 9-fold that of heat extraction alone. All bovine lung gelatin contained protein as the major proximate component, with little ash and non-detectable fat. Bovine lung gelatin had pH, moisture and protein comparable to or less than that of commercial bovine gelatin and decreased ash. Transmittance of bovine lung gelatin was substantially reduced compared to that of commercial bovine gelatin but had increased water and fat-binding capacity, and comparable or increased gelling and melting temperature. Gel strengths of bovine lung gelatin were comparable to or lower than and foam stability and emulsifying activity were lower than commercial bovine gelatin. Increased imino acid (proline and hydroxyproline) content was associated with increased gelling and melting temperatures and was comparable to commercial bovine gelatin. Heat-extracted bovine lung gelatin contained predominantly collagen γ– chains, β– chains and α– chains (α1(I) and α2(I)), with some low molecular weight peptides, while the pepsin-extracted lung gelatins were characterized by comparatively decreased β– and α– chains and increased low molecular weight peptides. The gel strength of heat-extracted bovine lung gelatin was higher than that of pepsin-extracted gelatins, indicating that additional yield was associated with reduced gelatin quality. Bovine lung is a potential source of gelatin for application in diversified industrial fields and use of pepsin is a viable method for extracting additional gelatin after heat extraction of high quality (increased gel strength) gelatin from bovine lung.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it