Myrrh Oleo‐Gum Resin as a Functional Additive in Pectin and κ‐Carrageenan Composite Films for Food Packaging
Bibliographic record
Abstract
ABSTRACT Myrrh oleo‐gum‐resin (MOGR) is a natural substance that has a rich history of medicinal use due to its anti‐inflammatory, antimicrobial, and antioxidant properties. The present study reports on the fabrication and assessment of pectin and K‐carrageenan composite films infused with varying proportions (0.3%, 0.5%, and 0.7%) of MOGR. Morphological analysis of the film samples was conducted using Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM). The results indicated that the introduction of MOGR led to a notable increase in surface roughness. The SEM micrographs of the films showed that the MOGR addition had an important effect on the microstructure of the film. The surface hydrophobicity of the MOGR‐loaded films increased, as confirmed by the rise in the contact angle. Moreover, there was an increase in the thickness (0.062 ± 0.004–0.095 ± 0.006 mm) and opacity (1.24 ± 0.07–9.41 ± 0.24) of the films with the addition of MOGR; however, tensile strength (7.30 ± 0.50–4.92 ± 0.34 MPa), elongation at break (32.41% ± 1.0%–29.70% ± 0.24%), and barrier properties decreased. Additionally, a rise in MOGR concentration corresponded to a rise in overall color difference Δ E (0.77 ± 0.03–5.09 ± 0.49) of the films. Notably, the incorporation of MOGR led to an increase in the antioxidant activity of the composite films, indicating potential applications in functional packaging materials.
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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".