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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".