Characterization of Caseinate–Carboxymethyl Chitosan-Based Edible Films Formulated with and without Transglutaminase Enzyme
Bibliographic record
Abstract
Edible composite packaging has the advantage of complementary functional properties over its individual bio-components. However, film composites made from caseinate (CA) and carboxymethyl chitosan (CMCH) have not yet been well explored. In this study, four types of CA-CMCH composite films were prepared and evaluated with and without transglutaminase (TGase) supplement. Aqueous CA (8%, w/v) and CMCH solutions (2%, w/v) were mixed in different volume ratios of CA: CMCH as 100:0, 75:25, 50:50, 75:25, and 25:75. Those to be supplemented with TGase were incorporated at 10 U/g of caseinate protein level. Results revealed that CMCH incorporation to CA facilitated a smooth and uniform surface microstructure on films and markedly improved the transparency, water barrier properties, mechanical properties, and solubility of the composite film. Furthermore, addition of TGase resulted in an improvement in the water vapor permeability. TGase successfully enforced the formation of CA-CMCH composites with some enhanced functional properties. The resulting composite film offers potential for applications as an alternative edible film or in the preparation of edible packaging films.
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 | 0.000 |
| 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) | 0.000 | 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 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".