An enzymatic approach for producing chitin from snow crab and American lobster seashells
Bibliographic record
Abstract
Shellfish processing plants generate large volumes of by-products rich in high-value biomolecules such as proteins, minerals, and chitin. Currently, chitin extraction is mainly carried out by chemical hydrolysis using strong acids and bases, a method with a large environmental impact. To address these concerns, a sustainable enzymatic approach was developed for extracting chitin from snow crab and American lobster shells. Different acid-active proteases were tested to identify the most effective enzyme for simultaneous deproteinization and demineralization of the shells. Pepsin demonstrated the highest efficiency, achieving over 95% demineralization and 78% deproteinization. The process was further optimized to minimize enzyme concentration and hydrolysis time. The respective optimum parameters for crab and lobster were 3130 and 2070 U of pepsin per gram of protein for 2.5 hours. In these conditions, deproteinization rates of 74.0% and 81.1% and demineralization rates of 94.5% and 90.8% were achieved for crab and lobster shells, respectively. Additionally, soluble proteins from the enzymatic hydrolysis were recovered and characterized, demonstrating their potential for use in animal feed and for bioactive peptides production. • Sustainable chitin extraction was achieved with efficient one-step pepsin hydrolysis • A second pepsin hydrolysis improved the purity of chitin • Shell soluble proteins were recovered and identified through proteomic analysis • Soluble shell proteins could be used as animal feed or bioactive peptides
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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 | 0.000 | 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".