Novel biological and chemical methods of chitin extraction from crustacean waste using saline water
Bibliographic record
Abstract
Abstract BACKGROUND Several techniques have been proposed to recover chitin from shrimp waste using large amounts of freshwater and chemicals. The standard chemical recovery of chitin was first compared by replacing fresh water with seawater. In addition, a biotechnological process with Bacillus subtilis (B1) and Bacillus licheniformis (B2) using seawater during all steps of chitin extraction was studied. RESULTS The demineralization rate (DM) was statistically significant when using seawater (P=0.000020) and chemical recovery in comparison with deproteinization (DP). Increasing HCl concentration (from 1 to 1.28 mol L−1) and reaction time (from 60 to 90 min) resulted in DM similar to fresh water (P>0.05). Highest DP rates were obtained with crude protease (B1; DP ≈74%) and (B2: DP ≈84%), when fermentation was carried out for 24 h at an enzyme/substrate ratio of 2. Maximum DP was reached (≈79% for B1 and ≈ 82% for B2) after 15 days, while DM ranged between 55 and 60%. CONCLUSION Combined enzymatic DP (with B2) followed by a chemical DM process was used to produce chitin (DP≈84%, DM≈94%) which, on transformation to chitosan, showed a degree of deacetylation equivalent to ≈ 71%. This combined approach using seawater could transform crustacean waste into chitin products of commercial value. © 2015 Society of Chemical Industry
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".