Porcine in vitro digestion and solubilization of non-starch polysaccharides in corn and wheat supplemented with xylanase and feruloyl esterase
Bibliographic record
Abstract
A study evaluated porcine in vitro digestion and non-starch polysaccharide (NSP) solubilization of corn and wheat without or with xylanase alone or xylanase plus feruloyl esterase (FE). The enzymes supplied 4000 U of xylanase and 35 U of FE per kilogram of sample. Samples were digested with pepsin at a pH of 2.0 and then with pancreatin at a pH of 6.8. The digested samples were filtered to obtain unhydrolyzed residue, which was then washed using alcohol and acetone. The cereal grains (CGs) before in vitro digestion and the washed unhydrolyzed residues were analyzed for dry matter and NSP. Xylanase improved ( P < 0.05) in vitro digestibility of dry matter (IVDDM) for wheat (85% vs. 89%), but not for corn (78% vs. 79%). Addition of FE to the xylanase-supplemented CGs increased ( P < 0.05) IVDDM for both CGs, and reduced ( P < 0.05) the arabinoxylans in the unhydrolyzed residue for corn, but not for wheat. In conclusion, xylanase product used in the current study is more effective in improving the digestibility of nutrients in wheat than in corn. The FE product used in the current study can improve the efficacy of xylanase in improving the nutrient digestibility of wheat and corn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".