Sensorial Properties, Chemical Characteristicsand Fatty Acids Profile of Cheese Fortified by Encapsulated Kilka Fish Oil
Bibliographic record
Abstract
However Kilka is a valuable fish in nutritional point of view, but a large part of it used in poultry feed. The main reason is the undesirable odor. In this study Kilka oil was blended with milk at 1% and 2% level and then the mixture spray dried. These encapsulated Kilka oil were added to cheese as a fortificant materials at 5% level. Cheese without encapsulated Kilka oil was as a control treatment. Results showed that there was no significant difference (p>0.05) between color of fortificated cheese with control cheese. There was no significant difference (p>0.05) between odor of cheese with 5% encapsulated Kilka oil that contain 1% Kilka oil (A) with control cheese. There was no significant difference (p>0.05) between flavor of cheese with 5% encapsulated Kilka oil that contain 1% Kilka oil (A) with cheese with 5% encapsulated Kilka oil that contain 2% Kilka oil (B) but there was significant difference (p<0.05) between these two treatments with control cheese. Also, the eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) content of fortified cheeses had significant difference (p<0.05) with control cheese.
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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) | 0.001 | 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".