Quality, Chemical Composition, and Amino Acids of Eggs in Lohmann Pink‐Shell Laying Hens and Dongxiang Green‐Shell Laying Hens
Bibliographic record
Abstract
Hen breed and age are key factors influencing egg quality. In this study, 250 eggs were categorised into five groups: Lohmann Pink-shell (LMP, 50 ± 2 weeks), Dongxiang Green-shell (DXG, 50 ± 2 weeks), Dongxiang Pink-shell (DXP, 50 ± 2 weeks), first-laid Dongxiang Green-shell (DXGF, 23 ± 1 week), and first-laid Dongxiang Pink-shell (DXPF, 23 ± 1 week). We compared egg quality traits, biochemical parameters, chemical composition, and amino acid profiles across these groups. Average egg weights were 59.89 g (LMP), 52.11 g (DXG), 59.45 g (DXP), 42.01 g (DXGF), and 42.56 g (DXPF). Yolk colour scores were higher in DXGF (13.72) than in LMP, DXG, and DXP (12.93, 12.72, and 12.81, respectively; p < 0.05). Egg yolk high-density lipoprotein (HDL) levels were lower in LMP, DXG, and DXP (0.0316, 0.0390, and 0.0334 mmol/g, respectively) than in DXGF and DXPF (0.0411 and 0.0424 mmol/g; p < 0.05). Yolk crude fat was higher in DXG, DXP, DXGF, and DXPF (50.00%, 49.29%, 49.54%, and 49.93%, respectively) than in LMP (47.70%; p < 0.05). The EAA/TAA ratios in yolk ranged from 54.53 to 55.33, while those in albumen ranged from 53.38 to 53.71. Overall, this study demonstrates that hen breed and age significantly affect egg quality, biochemical traits, and amino acid composition. These findings may guide consumer egg selection and support the conservation and efficient utilization of chicken genetic resources.
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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 | 0.001 | 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) | 0.000 | 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 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".