Preparation and Physiochemical Characterization of Bitter Orange Oil Loaded Sodium Alginate and Casein Based Edible Films
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Bibliographic record
Abstract
Biopolymers-based composite edible films are gaining interest in the food packaging industry due to their sustainable nature and diverse biological activities. In the current study, we used sodium alginate (SA) and casein (CA) for the fabrication of composite film using the casting method. We also added orange oil to the edible film and assessed its impact on the biological, chemical, physical, and barrier properties of the films. The fabricated films were analyzed using X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR). It was observed that CA-SA films loaded with 1.5% OEO had better visual attributes, and a further increase in oil concentration was not found to be as favorable. Mechanical assessment of the films revealed that CA-SA-OEO (1.5%) film showed lower puncture deformation and higher puncture force values. XRD data showed that all samples exhibited peaks at similar positions (21° of 2θ) with different intensities. In FTIR analysis, characteristic peaks of the film components (sodium alginate, casein, and orange oil) were reported at corresponding positions. The thermal stability of films was enhanced after the addition of the OEO (1.5%), however, a greater increase in OEO caused a decrease in the thermal stability, observed during TGA analysis. Moreover, the surface of the blank CA-SA film (FL1) was found to be rough (with cracks) compared to CA-SA films (FL2) containing 1.5% OEO. Additionally, FL2 was found to be relatively better than the other samples in terms of swelling degree (SD), thickness, water solubility (WS), oxygen permeability (OP), water vapor permeability (WVP), moisture content (MC), and transparency (T).
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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 | 0.000 | 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 it