Low-Cost Production of Chitosan Biopolymer from Seafood Waste: Extraction and Physiochemical Characterization
Bibliographic record
Abstract
Chitosan is an abundant natural biopolymer widely used in industrial and pharmaceutical applications. It stands out for its remarkable biodegradability, biocompatibility, and versatility. Herein, we tried to extract chitosan from mud crab (Scylla spp.), a seafood waste abundantly found in Bangladesh’s growing crab farming industry, via a simple low-cost production route. At first, chitin was extracted from crab shells through demineralization and deproteinization to eliminate minerals and proteins. The chitosan biopolymer was then obtained by deacetylation of purified chitin. To evaluate its physicochemical properties, the as-prepared chitosan was characterized by different analyses, such as water and fat binding capacity, solubility, viscosity, molecular weight, fourier transform-infrared, thermogravimetric, scanning electron microscopy, and ash content analysis. The results showed that the crab shell contains around 26.8% chitosan by dry weight, making it an excellent raw material for the massive production of the natural biopolymer chitosan. The prepared chitosan showed fat and water binding capacities of 200-300% and ~680.9%, respectively. Furthermore, it was highly soluble in 1% acetic acid and had an ash content of about 33.7%. Convincingly, the produced chitosan showed great physiochemical properties making it suitable for biomass efficiency, sustainable development, revenue generation, and biomedical applications. In addition, the recycling of seafood waste into a valued product is beneficial to help keep the environment clean, which is among the sustainability goals in Bangladesh and globally.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".