Development of Tailored Composite Biopolymer Film Formulations Using Minimally Refined Chitosan from American Lobster (Homarus americanus) Shell Waste for Different Food Packaging Applications
Bibliographic record
Abstract
The need for sustainable alternatives to petroleum-based plastic packaging has prompted interest in biodegradable biopolymer films. This study developed edible composite films using minimally refined chitosan from American lobster (Homarus americanus) shell waste combined with fish gelatin, glycerol, and sunflower oil. A Box–Behnken design within a response surface methodology (RSM) framework was used to investigate the effects of these formulation variables on ten key film properties, including mechanical strength, water sensitivity, barrier performance, and optical characteristics. High-quality empirical models (R2 ≥ 0.88) captured nonlinear, synergistic, and antagonistic interactions among the components, revealing trade-offs between competing attributes. Simultaneous multi-response optimization identified balanced formulations suited to various food packaging needs, including perishable, fresh, and dry products. Experimental validation of selected formulations confirmed model predictions within 5% error under laboratory conditions. Up to 68% of the inhibition activity against Escherichia coli was retained in a few composite formulations when compared with neat chitosan films, thus supporting their potential for active packaging. The key highlight of the present work is the use of crude chitosan derived from lobster shell waste, a low-cost, sustainable alternative to highly purified commercial sources, demonstrating the practical viability of marine byproduct valorization. Overall, this study advances the development of high-performance, application-specific biopolymer films and highlights RSM as an effective tool for optimizing multifunctional edible packaging materials. Future work should focus on enhancing antimicrobial functionality, evaluating real-world performance, and assessing consumer acceptance to support industrial adoption.
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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".