EXTRACTION, FRACTIONATION AND ACTIVITY CHARACTERISTICS OF PROTEASES FROM SHRIMP PROCESSING DISCARDS
Bibliographic record
Abstract
Proteases recovered from Northern pink shrimp (NPS) and Southern rough shrimp (SS) processing discards (heads, shells, tails) were characterized. Shrimp processing discards were extracted with water following homogenization and centrifugation in order to obtain the crude extract which was subsequently fractionated with solid ammonium sulfate. Two fractions sedimenting with 30–50% (designated as NPS-I and SS-I), and 50–70% (designated as NPS-II and SS-II) ammonium sulfate were collected following centrifugation, respectively. Endoprotease activity of the crude extract was 0.02 U/mg for hemoglobin (Hb, pH 3.0), 0.16 U/mg for azocasein (Ac, pH 6.0) and 0.12 U/mg for benzoyl-Arg-β-naphthylamide (BANA, pH 7.0). The exoprotease activity was 0.11–0.17 U/mg for Arg-β-naphthylamide (ArgNA, pH 7.0), 0.06–0.11 U/mg for Lys-β-naphthylamide (LysNA, pH 7.0) and 0.08–0.09 U/mg for Leu-β-naphthylamide (LeuNA, pH 7.0). Endoprotease activity increased 5–7.4 fold for NPS-I and SS-I, and 1.9–2.7 fold for NPS-II and SS-II against crude extract. Meanwhile, exoprotease activity increased 3.6–4.8 fold for NPS-I and SS-I, and 5.6–7.2 fold for NPS-II and SS-II. Meat treated with NPS-I and SS-I was tenderized more extensively than that treated with NPS-II and SS-II. The results of this study suggest that proteases recovered from shrimp processing discards may potentially be used as processing aids in formulated foods.
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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) | 0.000 | 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".