Bibliographic record
Abstract
During frying, fat degrade and many reactions occur resulting in numerous altered fatty acid products. This would lead to the formation of <i>Tran's</i> fatty acids and changes in nutritional qualities. Hence, this study was aimed to determine the changes during frying on fatty acid composition of Nile tilapia (<i>Oreochromis Niloticus</i>) fish muscle from local fish market of Hawassa, Ethiopia. Fish fryers reported that they usually use palm oil for frying. They also indicated that the oil is kept for days and reused for frying at different cycle. In fried fish, 35 types of fatty acids were identified and 27 in raw fish muscle. Palmitic, stearic, heptadecanoic, and tetradecenoic acids were the abundant saturated fatty acids in both fried and raw fish muscle. Oleic, Docosahexaenoic, Eicosapentaenoic, and Linoleic acid were the major unsaturated fatty acids. The percentages of total saturated fatty acids (SFA) of raw fish muscle (47.4%) were found to be lower compared with fried fish muscle (51.8%). The <i>n</i>-6<i>/n</i>-3 ratio (7.83), index of atherogenicity (0.85), and thrombogenicity (1.71) in the fried fish muscle were in the undesirable values. The hypocholesterolemic/hypercholesterolemic ratio was relatively similar in the fried (1.09) and raw (1.02) fish muscles. The total unsaturated fatty acids (UFA) level of raw fish muscle (52.2%) was higher than the fried fish muscle (47.2%). Higher percentage of <i>Tran's</i> fatty acid was identified in the fried fish muscle compared with raw. Frying considerably altered fatty acid composition of fish muscle. It increased saturated fatty acid concentration and decreased unsaturated. Hence, frying noticeably decreases the nutritional quality of fish muscle. Therefore, it is suggested that further analysis on fatty acid composition of oil usually used for frying and the nutritional quality index across frying cycles.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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".