Effect of protein hydrolysis on the emulsifying properties of collagen and its hydrolysates extracted from leather by‐products
Bibliographic record
Abstract
Abstract Background Proteins are widely used as emulsifiers due to their amphiphilic nature. Collagen has been of particular interest due to both its distinctive triple helical structure and unique amino acid arrangement, which gives it strength and stability within the water (hydrophilic) and oil (hydrophobic) domains of the emulsion. Hydrolysing collagen reveals varying amino acid side residues and alters its interaction with water/oil interfaces. This study aimed to explore how the degree of hydrolysation influences the protein's ability to act as an emulsifier, focusing on hydrolysed collagen derived from leather industry by‐products. Results The results revealed that the optimal degree of protein hydrolysis was dependent on both the environment and the length of stability required of the emulsion. In this work, the least hydrolysed collagen sample had the most versatile properties in both cool and hot conditions, however due to the larger molecular size of the protein it also had a lower stability when measured over an extended time. A more hydrolysed form of collagen formed a stable emulsion at cool temperatures, however the breakdown of the emulsion observed at 40 °C indicated the need for further protein purification. Further hydrolysing the collagen caused a breakdown of the liquid emulsions at 4 °C due to gel formation, however this form of collagen performed best when held at elevated temperatures. Conclusion The results of this study concluded that the degree of hydrolysation of collagen could be manipulated to produce a tailored emulsifying agent dependent on the desired commercial end‐use. © 2025 Scottish Leather Group and The Author(s). Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".