Effect of Soybean Oil on the Improvement of the Functionality of Edible Membrane-Type Food Packaging Films Based on Caseinate–Carboxymethyl Chitosan Compositions
Bibliographic record
Abstract
Edible film biopolymers are gaining attention to tackle problems of plastic waste and food safety to alleviate environmental problems associated with plastic products in food packaging. In this study, caseinate–carboxymethyl chitosan (CA–CMCH) composite films were made with the incorporation of soybean oil (SO) using a casting technique. The influence of different soybean oil concentrations at 0, 0.5, and 1% (w/w) on physical, mechanical, barrier, and surface characteristics of films composed of caseinate–carboxymethyl chitosan (CA–CMCH) was evaluated. The brightest film (L* value of 95.95 ± 0.30) was obtained with the edible film made from the control group of samples with sodium caseinate (NaCA-100; 100% NaCA). The results also indicated that samples with 1% SO in NaCA-75 and CaCA-75 had lower water vapor permeability (WVP), while those with NaCA-50 and CaCA-50 showed higher values of WVP. For mechanical properties, this study found that incorporating soybean oil into the caseinate–carboxymethyl (CA–CMCH) composite films led to an enhancement of both tensile strength and elongation at break. The morphological structures, determined using SEM, of control and composite films showed compact and homogenous surfaces. Overall, the addition of soybean oil contributed to the improvement of the functional properties of the edible films, offering potential solutions to the environmental issues associated with plastic packaging and enhancing the safety and performance of food packaging.
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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".