Omega-3-enriched broiler meat: 3. Fatty acid distribution between triacylglycerol and phospholipid classes
Bibliographic record
Abstract
Consumers are becoming more aware of the effect of the food they eat on their health. One of the ways they hope to reduce their risk of cardiovascular disease is by consuming more foods enriched with n-3 polyunsaturated fatty acids (PUFA). Due to the high content of alpha-linolenic acid (LNA), dietary flaxseed is a good source for increasing n-3 PUFA in poultry meat. A study was conducted with 2 primary objectives: to establish the distribution of n-3 PUFA between triacylglycerol (TAG) and phospholipid of broiler chicken breast and thigh meat and to determine the duration of dietary flaxseed supplementation required to ensure a level of n-3 PUFA of 300 mg per 100 g of meat necessary to label meat as a source of n-3 PUFA. This experiment was conducted as a 2 x 8 factorial, with 2 dietary levels of ground flaxseed (10 and 17%) and 8 durations of dietary flaxseed before processing [0 (control), 4, 8, 12, 16, 20, 24, and 35 d]. A total of 128 Ross x Ross 308 mixed-sex broilers were evaluated to 35 d of age. Breast and thigh meat fatty acid composition was analyzed on duplicate samples of ground meat pooled from 8 birds per treatment. Broken-stick analysis was used to estimate the duration required to achieve 300 mg of n-3 PUFA per 100 g of breast meat. Results clearly indicated that LNA was mainly deposited in the TAG fraction of both breast and thigh meat. Enriching the chicken breast meat with 300 mg of n-3 PUFA per 100 g of meat was achieved in 11.3 and 26.2 d with a 17 and 10% level of flaxseed in diet, respectively. Although a significant increase of n-3 long-chain PUFA (20:5n-3, 22:5n-3, and 22:6n-3) was found in the phospholipid and TAG fraction of both tissues, the concentration of these functional components was low. More than 95% of n-3 PUFA enrichment was due to LNA.
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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".