Characteristics of Salt-Fermented Sauces from Shrimp Processing Byproducts
Bibliographic record
Abstract
A salt-fermented sauce from shrimp processing byproducts (heads, shells, and tails) was prepared and characterized. Three types of sauces were prepared; sauce C, with 30 g of salt/100 g of byproduct (high salt); sauce E, with 30 g of salt and 0.2 g of sodium erythorbate (high salt); and sauce L, with 20 g of salt, 0.2 g of sodium erythorbate, 6 g of sorbitol, 0.5 mL of lactic acid, and 5 mL of ethanol (low salt). Sauces C and E showed higher exopeptidase activities than sauce L, whereas sauce L showed the highest endopeptidase activity. After 3 months of fermentation, the amino N content of sauce increased from 150-200 to 500-600 mg/100 g and the nonprotein nitrogen content increased from 300 to 950-1050 mg/100 g. Volatile basic nitrogen content increased significantly from 18 to 60 mg/100 g. The total carotenoids retained in sauces C, E, and L were 26.3, 76.2, and 73%, respectively, thus indicating that the addition of sodium erythorbate to sauces E and L retarded oxidation. Water activities of sauces C, E, and L were 0.753, 0.751, and 0.773, respectively. According to the omission test, the taste of sauces was influenced by the content of free amino acids, mainly glutamic acid and aspartic acid. All three sauces examined showed a 35% higher total amino acid content than commercial salt-fermented shrimp sauces. Therefore, shrimp processing byproducts may lend themselves to the preparation of high-quality salt-fermented sauces.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".