Effects of dehairing treatment on gelatin yield and quality from bovine hides
Bibliographic record
Abstract
Abstract Purpose Hides are the by product of slaughter houses which are mostly used for leather production. In Canada, the hides are either disposed of with other slaughter waste or sold at a very low price. Dehairing of hides is a prerequisite for either leather or gelatin production from it. Therefore, the effect of hide dehairing method on subsequent gelatin extraction and quality was investigated. Methods Bovine hides (BH) were dehaired using either 5% acetic acid (AA), 10% calcium hydroxide (CH), 0.02% keratinase (KTN), 2.5% papain (PP), or not at all (control; CT), with control BH subsequently treated with 5% AA (CTAA). Results Mean bovine hide gelatin (BHG) yields (dry basis) were 11.37%, 54.25%, 45.07%, 18.88% and 55.02% for CT, AA, CTAA, CH, and KTN, respectively. Gel strength was highest in AA followed by the CTAA, CT and CH and KTN treatments. The molecular weight (MW) distribution pattern showed that dehairing of BH with enzymes degraded the collagen extensively, increased proportions of low MW peptides that translated into low gel strength. Conclusions Acetic acid, which is extensively using in food industry, can be used to dehair BH as pre-treatment to extracting high quality gelatin.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".