Designer laying hen diets to improve egg fatty acid profile and maintain sensory quality
Bibliographic record
Abstract
The fatty acid composition of eggs is highly reflective of the diet of the laying hen; therefore, nutritionally important fatty acids can be increased in eggs in order to benefit human health. To explore the factors affecting the hen's metabolism and deposition of fatty acids of interest, the current research was divided into two studies. In Study 1, the fatty acid profile of eggs from Bovan White hens fed either 8%, 14%, 20%, or 28% of the omega-6 fatty acid, linoleic acid (LA) (expressed as a percentage of total fatty acids), and an additional treatment of 14% LA containing double the amount of saturated fat (SFA) was determined. Omega-6 fatty acids and docosapentaenoic acid (DPA) in the yolk were significantly (P < 0.05) increased, and oleic acid (OA) and eicosapentaenoic acid (EPA) were significantly decreased with an increasing dietary LA content. In Study 2, the fatty acid and sensory profiles were determined in eggs from Shaver White hens fed either (1) 15% or 30% of the omega-3 fatty acid, alpha-linolenic acid (ALA) (of total fatty acids), and (2) low (0.5), medium (1), or high (2) ratios of SFA: LA+OA. Increasing this ratio resulted in marked increases in lauric acid, ALA, EPA, DPA, and docosahexaenoic acid (DHA), with decreases in LA and arachidonic acid. Increasing the dietary ALA content from 15% to 30% (of total fatty acids) did not overcome the DHA plateau observed in the yolk. No significant differences (P ≥ 0.05) in aroma or flavor between cooked eggs from the different dietary treatments were observed among trained panelists (n = 8). The results showed that increasing the ratio of SFA: LA+OA in layer diets has a more favorable effect on the yolk fatty acid profile compared to altering the LA content at the expense of OA, all while maintaining sensory quality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".