Effect of chitosan on the heat stability of whey protein solution as a function of <scp>pH</scp>
Bibliographic record
Abstract
BACKGROUND: Chitosan was reported to interact with proteins through electrostatic interactions. Their interaction was influenced by pH, which was not fully characterized. Further research on the interactions between protein and chitosan at different pH and their influence on the thermal denaturation of proteins is necessary. RESULTS: In this research, the effect of chitosan on the heat stability of whey protein solution at pH 4.0-6.0 was studied. At pH 4.0, a small amount chitosan was able to prevent the heat-induced denaturation and aggregation of whey protein molecules. At higher pH values (5.5 and 6.0), whey proteins complexed with chitosan through electrostatic attraction. The formation of chitosan-whey protein complexes at pH 5.5 improved the heat stability of dispersions and no precipitation could be detected up to 20 days. The dispersion with a medium amount of chitosan (chitosan:whey protein 1:5) produced the most stable particles, which had an average radius of 135 ± 14 nm and a zeta potential value of 36 ± 1 mV. In contrast, at pH 6.0 only the dispersion with a high amount of chitosan (chitosan:whey protein 1:2) showed good shelf stability up to 20 days. CONCLUSIONS: It was possible to produce heat-stable whey protein beverages by regulating the interaction between chitosan and whey protein molecules. © 2016 Society of Chemical Industry.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".