Fish protein hydrolysate production, treatment methods and current potential uses: A review
Bibliographic record
Abstract
About 20 per cent of the live weight of freshwater fish is visceral mass is a rich source of protein, lipids, polyunsaturated fatty acids, soluble vitamins, phospholipids etc having very great source of agro industrial products. Any scientific technique that uses strong squanders coming about because of fish harvesting by recuperating biomolecules like lipids and proteins brings about extra income as well as helps to reduce the dumping issues related therewith. Visceral fish hydrolysate (also known as fish protein hydrolysate; FPH) has drawn the interest of many researchers in recent years due to its great utility in the fields of food, pharmaceuticals, cosmetics and nutrition. The extraction or production of FPH including predefined sequential steps like collection of raw materials, pretreatment (alcohols, heat treatment, press technique coupled with heat treatment), hydrolysis (acidic, alkaline, enzymatic) and recovery of FPH using spray drying, lyophilisation, centrifugation or nanofiltration. The extracted FPH is characterized for molecular mass, protein content, structure of protein, IR, XRD etc to analyse the chemical nature of the FPH. It is widely used as emulsifier, binder, gelling agent, fertilizer, crayoprotectant and dietary additives in various industries. FPH has been used as potential source of microbial growth media for gram positive and negative microbes, nutritional supplement due to higher protein content (60-90%), and antioxidant, antihypertensive and antimicrobial agent. FPH is also used as nutriceuticals and some of the products are commercially available in various countries including US, Canada, UK. Considering the physicochemical difficulties associated with FPH, its excellent nutritional and functional properties generate possibilities for its usage in both the food and health industries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".