Extraction, Characterization and Utilization of Fish Protein Concentrate
Bibliographic record
Abstract
Progress in investigation for extraction of fish protein concentrate (FPC) by different solvents (acetone, isopropyl alcohol and hexane: ethanol azeotropic mixture as Canadian, British and Indian method) of pink perch (Nemipterus spp.) analyzed for their functional properties and further utilized in developing value added product. FPC extracted by isopropyl alcohol was superior with significantly (P<0.05) highest protein, lowest moisture and fat content. Significantly (P<0.05) superior viscosity (118.37 cP), Protein solubility (84.40%), Emulsification capacity (68.53%), Emulsification stability (60.92%), Foaming capacity (140.01%), Foaming stability (121.70%) and Water holding capacity (25.35 mL/g) for isopropyl alcohol compared with other solvents. Due to typical fishy odor fish meat replaced with FPC (0, 5, 10, 15 and 20 %) in fish finger formulation with FPC incorporation at 10% levels were most acceptable. Strong linearity (R2) for changes biochemical values (R2>0.96) and microbiological values (R2=0.9705) were found. Sensory evaluation had a negative correlation (R2>0.814) during storage suggesting shelf life of 11 days. Suggesting utilization of low value fishes for extraction of high quality proteins and their application for development of novel 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) | 0.000 | 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".