PSXVI-10 Influence of Carbohydrate Rich co-Products and Fiber on Growth Performance and fat Quality in Finishing Pigs
Bibliographic record
Abstract
Abstract In recent years, different strategies have been undertaken to reduce feed cost including incorporation of co-product. However, addition of large quantity of co-product can influence meat quality, especially fat quality. The main objective of the present study was to assess the effect of feeding high dairy/carbohydrate diets on growth performance and fat quality in finishing pigs.A total of 36 pigs [Yorkshire x Landrace(xDuroc)] were raised in commercial diets up to 75kg. At this time, pigs were distributed in six dietary treatments applied for 4 weeks. First three treatments were: a control diet (C) consisting of a corn-soybean meal diet, 25CoP containing 25% co-products (sucrose, whey and bakery meal) and 50CoP containing 50% co-products. Three other diets were formulated from 50CoP and supplemented with three sources of fiber: oat hull (50Oat),soy hull (50Soy) and wheat middling (50 Midd). Feed intake and average daily gain were recorded. After 4 weeks, pigs were slaughtered and a 5-cm strip of backfat was taken from each animal. A measurement of thermodynamic characteristics (melting behavior and solid fat content) were determined by differential scanning calorimetry. ADG and ADFI were reduced in 25CoP compared with 50CoP with intermediary value for C (P< 0.05). 50Soy had decreased ADFI while 50Oat had greater feed conversion than 50CoP (P< 0.05). Only meat lean yield tended to be increased in 25CoP and 50Soy compared with 50CoP (P< 0.09). Melting point onset and maximum were higher in 50CoP compared with C and 25CoP (P< 0.05). Fiber supplementation reduced these melting points compared with 50CoP (P< 0.01). Proportion of fat melted at -5°C and 5°C was decreased in 50CoP compared with C and 25CoP. Fiber increased these proportions of melted fat (P< 0.01). This project shows that diet containing around 50% of rich carbohydrate co-product can negatively affect fat quality and fiber supplementation to reach 12% NDF restore the fat 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) | 0.000 | 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".