Development of High Nutritious Fish Powder Mixture Using <em>Chirocentrus dorab, Selaroides lepotolepis</em> and <em>Stolephorus indicus</em> for Bakery Industry
Bibliographic record
Abstract
The research was aimed to develop a highly nutritious fish powder mixture by incorporating dried fish powder of small fish, Chirocentrus dorab (Katuwalla), Selaroides lepotolepis (Suraparawa) and Stolephorus indicus (Hadella) to enhance the nutritional value. Raw fish was collected from the fishing area of Chillaw and Galle in Sri Lanka. The developed fish powder mixture is a potential ingredient to enrich the nutritional value of wheat flour mixtures or dough prepared in the bakery industry conveniently. The fish powder mixture formulae were developed by mixing with different ratios of selected small dry fish powder. The best fish powder formulation was selected using sensory evaluation conducted using fish powder incorporated wheat flour buns. Further, in order to enhance the odor of fish powder mixture, natural ingredients such as clove, almond essence and vanilla were separately incorporated into the selected fish powder formulation and they were evaluated by a sensory panel. Of them, clove was selected as the best odor enhancer by the panelists. The proximate composition of selected fish powder showed 5.98% of moisture, 4.44% of ash, 6.98% of fat and 44.25% of protein, 9.23% of crude fiber and 29.16% of carbohydrates, respectively. In microbiological tests, no colonies of E. coli and Staphylococcus aureus were detected in fish powder. Further, to verify the shelf life of fish powder, Aerobic Plate Count was performed at ambient temperature and without adding any natural or artificial preservatives into fish powder. The shelf life of fish powder was three weeks at ambient temperature. In conclusion, the developed nutritious fish powder has high potential as a low cost, convenient ingredient to enhance the protein content in bakery products. Further studies on extended shelf life determination by adding natural preservatives could be recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".