Omega-3 enriched broiler meat: The influence of dietary α-linolenic-ω-3 fatty acid sources on growth, performance and meat fatty acid composition
Bibliographic record
Abstract
Western diets are typically low in ω-3 fatty acids, and high in saturated and ω-6 fatty acids. There is a need to increase dietary ω-3 fatty acid content. Chia (Salvia hispanica L.) has the highest botanical source of alpha-linolenic acid (ALA) known, and recently has been receiving more attention because of this. Feeding ALA to animals has been shown to increase the ω-3 fatty acid content of the foods they produce, and hence offers consumers an easy way to increase their intake of ω3 fatty acids without altering their diet. Broilers were fed rapeseed, flaxseed, chia seed and chia meal to assess the ability of these feed ingredients to increase the ω-3 fatty acid content of the meat, and also to determine whether any negative effects on bird production would arise. Flaxseed produced significantly (P < 0.05) lower body weights, weight gains and poorer conversion ratios than did the other feeds. Except in the case of the chia meal with the dark meat, the chia seed significantly (P < 0.05) reduced the saturated fatty acid (SFA) content of the white and dark meats compared with the control diet. Adding ALA increased the ALA, LCω-3 fatty acid and total polyunsaturated fatty acid (PUFA) ω-3 fatty acid content of both meat types, except in the case of the white meat of the birds fed rapeseed. Chia seed gave the highest total PUFA ω-3 increase, yielding 157 and 200% increases for the dark and white meat, respectively, compared with the control. The ω-6:ω-3 and SFA:ω-3 ratios dramatically improved in both types of meat when chia seed, chia meal or flaxseed was added to the diet. The study also showed that not all ALA-rich seeds are biologically equivalent sources in terms of producing ω-3 enriched broiler meat. Chia proved to be superior to the other sources examined in this trial. Key words: Chia seed, flaxseed, rapeseed, omega-3, alpha-linolenic, broiler meat, fatty acid
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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 | 0.001 |
| 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".