Valorization of mango byproducts for sustainable active packaging: Advances in functionalized biopolymer films
Bibliographic record
Abstract
Faced with the environmental crisis caused by plastic waste, recent research has focused on the development of biopolymer-based food packaging films with enhanced properties, among which agricultural byproducts are promising resources. This review comprehensively analyzes the application value of different mango byproducts-peel, seeds, and leaves-in sustainable active packaging films. It evaluates the latest progress in utilizing these wastes: peel phenolics and pectins can enhance antioxidant, UV-protective, and smart pH-responsive properties; seed starch, kernel extracts, and oils can improve mechanical strength, thermal stability, and hydrophobicity; and leaf bioactives exhibit strong antimicrobial functions. This work innovatively synthesizes strategies for extracting these functional components and integrating them into various biopolymer matrices (e.g., chitosan, polylactic acid, starch), demonstrating their efficacy in significantly extending the shelf life of perishable foods. This study highlights mango byproducts as a versatile and environmentally friendly alternative that aligns with circular economy principles and has the potential to transform waste into high-value packaging materials-despite challenges in standardization and scalability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".