Development of Bioactive Agar-Based Bioplastics Enriched with Cocoa Pod Husk Extract: Structural, Barrie, and Antioxidant Properties
Bibliographic record
Abstract
The growing demand for eco-friendly food packaging has driven the development of biodegradable active films. This study developed novel agar-based films incorporating phenolic-rich cocoa pod husk (CPH) extract (10 - 25% w/w) and glycerol (30–50% w/w) using green deep eutectic solvent (DES) extraction. The optimal formulation (25% w/w CPH, 40% glycerol) demonstrated exceptional functionality. The antioxidant activity of bioplastics added with phenolic compounds of cocoa pod husk extract at 10, 15, and 25% w/w, analysed by the DPPH method, obtained results of 45.72 ± 0.27, 47.45 ± 0.06, and 56.85 ± 0.06, respectively, and the water vapour permeability was 907.26 g/m 2 day. The hydrogen bonding interaction among the components of the blend films led to enhanced structural, barrier, thermal stability, miscibility, antioxidant properties, and smooth surface morphology of the blend films. Characterization showed hydrogen bonds such as -OH between agar, glycerol, and phenolic CPH, C=O ester groups (FTIR), and homogeneous microstructure (SEM). DES extraction preserved bioactivity better than conventional methods. This study demonstrates the potential of agar/CPH films as sustainable active packaging for perishable foods, combining antioxidant/antimicrobial properties with mechanical strength, while addressing both food preservation and environmental sustainability challenges through circular economy principles.
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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) | 0.000 | 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".