Optimization of Enzymatic Deproteination of Northern Shrimp (Pandalus borealis) Shell Chitin Using Commercial Proteases
Bibliographic record
Abstract
Shrimp shells are a key source of chitin, commonly extracted through chemical methods, which may cause minor molecular damage. Nowadays, there is great interest in achieving close to zero protein content in crude chitin in order to use it for high-end markets. Therefore, this study optimized the enzymatic deproteination using two commercial proteases (SEB Pro FL100 and Sea-B Zyme L200) for effective and fast removal of residual protein from Northern shrimp (Pandalus borealis) shell chitin for the first time. The protein content was determined using both the Kjeldahl method and amino acid analysis using gas chromatography–mass spectrometry (GC-MS). The performance of papain (Sea B Zyme L200) was superior to fungal protease (SEB Pro FL100) for this application, and it achieved residual protein content of 2.01%, while the calculated optimum for the latter enzyme was 6.18%. A model was developed using 24 factorial design, and it was predicted that the lowest residual protein content using fungal protease and papain could be achieved at the following conditions: a pH of 4.2 and 7, and an enzyme concentration of 4 and 1.5%, respectively. Thus, the low-protein content obtained using enzymatic deproteination could be an alternative approach to the traditional methods, indicating their potential to produce premium-quality chitin.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".