Rheological and structural study of electrostatic cross-linked xanthan gum hydrogels induced by β-lactoglobulin
Bibliographic record
Abstract
This study examines for the first time the role of xanthan gum (XG) and β-lactoglobulin (βlg) in network formation induced by electrostatic attractive interaction. The gelation processes of βlg–XG mixtures were monitored by viscoelastic measurements as a function of the βlg–XG ratio and βlg and XG concentrations. The structural characterization of the gels was addressed by means of confocal laser scanning microscopy. It was found that the initial tenuous network of XG provided a frame for gel organization, the βlg aggregated along the XG chains and could be regarded as a crosslinking agent, and more elastic gels were obtained at high XG concentrations. The lowest XG concentration at which gelation is possible is estimated to be 4.8 × 10−3 wt%. The βlg–XG ratio strongly affects gelation kinetics and is a main factor controlling the gelation process and the gel structure. The optimal ratio for electrostatic interaction at final pH of gels was estimated to be approximately 3.5. The decreasing repulsive interaction between XG chains and the increasing attractive interaction between βlg and XG with decreasing pH resulted in the formation of soluble complexes followed by the formation of interpolymer complexes. The electrostatic cross-linking of XG chains by βlg results in a sol–gel transition at the point of gelation. This mechanism may be applicable in a wide variety of protein–polysaccharide systems in which the structure of these systems is mainly stabilized by electrostatic attractive interaction.
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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 | 0.000 |
| 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 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".