The differential effects of cooking methods on the nutritional properties and quality attributes of meat from various animal sources
Bibliographic record
Abstract
This study investigated the various effects of two food product processing methods (boiling and grilling) on the nutritional composition (fatty acid, amino acid profiles) of meat from cows, goats, and rabbits. Freshly slaughtered animals were cleaned and subjected to boiling and grilling. Cooking loss varied with cooking methods; grilling resulted in the highest cooking loss, especially in cow meat (52.95%). Data from the proximate composition analysis revealed that both raw and grilled meat samples of rabbit meat contained the highest amount of protein (22.93 and 22.20 %, respectively) when compared to the corresponding samples from the other two animal sources. Additionally, rabbit meat contained a low level of fat (1.85%), which was not significantly different than the boiled samples (1.75, 1.76 %). Boiling and grilling significantly increased the in vitro protein digestibility of meat. The meat showed significant sources of both essential and non-essential amino acids. Rabbit meat showed a higher proportion of essential amino acids and a higher protein efficiency ratio. Boiled goat meat had a lower proportion of saturated fatty acids (SFA), boiled meat had higher polyunsaturated fatty acids (PUFA) than its grilled counterpart. Goat meat showed a favourable fatty acid profile. Thus, goat and rabbit meat are healthier alternatives to beef, and both boiling and grilling are useful in maintaining the nutritional qualities of meat.
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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) | 0.000 | 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".