Sustainable development of chitosan-gelatin composite films for food packaging using crude chitosan extracted from American lobster (Homarus americanus) shell waste
Bibliographic record
Abstract
• Crude chitosan from the shell waste of lobsters formed solvent-cast edible films. • Blends of gelatin, oil and crude chitosan enhanced the composite film properties. • Changes in the drying temperature had a significant effect on film properties. • Crude chitosan and fish gelatin formed polyelectrolytic complexes. • Oil improved the hydrophobicity of films without impacting mechanical properties. In this study, crude chitosan extracted from American lobster shells ( Homarus americanus ) was used to develop bio-based films. These films were prepared using solvent casting at various drying temperatures (37, 60, and 80 °C) and enhanced by blending with sunflower oil and gelatin from fish skin. Structural and thermal analyses (FT-IR, XRD, and TGA) demonstrated excellent compatibility between the extracted chitosan and fish gelatin. A 1:1 blend of these biopolymers resulted in films with increased flexibility and reduced water vapor permeation compared to pure chitosan films. Adding 10 % (w/w polymer) sunflower oil improved surface hydrophobicity and reduced swelling without compromising strength. Higher drying temperatures increased swelling, stretchability, and opacity, but decreased tensile strength and vapor permeability. Films prepared at 80 °C exhibited the best elasticity (100.4 %) and the lowest vapor permeability (1.2 g.mm/kPa.h.m 2 ) while maintaining adequate tensile strength (27 MPa). This study demonstrates a sustainable approach for developing chitosan-gelatin composite films using renewable marine processing byproducts. In particular, the applicability of crude chitosan extracted from waste lobster shells (without additional purification steps) as a cost-effective and sustainable feedstock is highlighted, offering an eco-friendly alternative to conventional plastic food packaging.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".