Effect of lecithin with or without chitooligosaccharide on the growth performance, nutrient digestibility, blood metabolites and pork quality of finishing pigs
Bibliographic record
Abstract
Two experiments were conducted to evaluate the effect of dietary lecithin with or without chitooligosaccharide (COS) on the performance, blood metabolites, pork cholesterol, fatty acid composition and quality of finishing pigs. In exp. 1, 36 pigs (Landrace × Yorkshire × Duroc, 84.5 ± 0.60 kg initial body weight) were fed lecithin at 0, 2.5 or 5.0% of the diet. Lecithin improved average daily gain (16%) and feed conversion ratio, and did not affect apparent nutrient digestibility. On day 28, lecithin decreased serum total and low density lipoprotein (LDL) cholesterol (34 and 77%, P = 0.016), and increased serum triglyceride (P = 0.048). Lecithin did not affect carcass characteristics and pork quality, but increased myristic and α-linolenic acid and reduced palmitoleic acid in pork. Experiment 2 involved 108 pigs (85.0 ± 0.76 kg initial body weight) in a 2 × 2 factorial arrangement of treatments, wherein two levels of lecithin (low, 2.5 and high, 5.0%) and COS (0.0 and 0.1%) were used. Addition of COS in diets containing lecithin reduced pork cholesterol (16.4%) and oleic acid (28.3%), and did not affect performance, nutrient digestibility, blood metabolites and pork quality. In conclusion, these results suggest that lecithin improved the growth performance of finishing pigs and inclusion of COS reduced the amount of cholesterol in pork. Key words: Lecithin, COS, performance, nutrient digestibility, pork 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".