Fruit waste valorization for biodegradable biocomposite applications: A review
Bibliographic record
Abstract
Currently, food waste is a major concern for companies, governments, and consumers. One of the largest sources of food waste occurs during industrial processing, where substantial by-products are generated. Fruit processing creates a lot of these by-products, from undesirable or “ugly fruit,” to the skins, seeds, and fleshy parts of the fruits. These by-products compose up to 30% of the initial mass of fruit processed. Millions of tons of fruit wastes are generated globally from spoilage and industrial by-products, so it is essential to find alternative uses for fruit wastes to increase their value. This goal can be accomplished by processing fruit waste into fillers and incorporating them into polymeric materials. This review summarizes recent developments in technologies to incorporate fruit wastes from sources such as grape, apple, olive, banana, coconut, pineapple, and others into polymer matrices to create green composites or films. Various surface treatments of biofillers/fibers are also discussed; these treatments increase the adhesion and applicability of the fillers with various bioplastics. Lastly, a comprehensive review of sustainable and biodegradable biocomposites is presented.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".