Effects of tannic acid and quebracho tannins on <i>in vitro</i> ruminal fermentation of wheat and corn grain
Bibliographic record
Abstract
Abstract Current understanding of the effect of moderate amounts of tannins on the utilization of protein contained in forages indicates beneficial effects when fed to ruminants. Nevertheless, there is little knowledge about the effects of tannins on the ruminal hydrolysis of starch‐rich concentrates. Modulation of the hydrolysis of starch may reduce the occurrence of metabolic disorders, such as acidosis, in ruminants consuming high‐grain diets. The effects of commercial tannic acid (hydrolysable tannins) and quebracho tannins (condensed tannins) (50 g kg−1 DM) on the in vitro fermentation of ground wheat and corn grains by mixed ruminal bacteria was examined. The architecture and chemical composition of wheat and corn endosperms might explain differences in fermentation rates, as well as in response to similar concentrations of tannins. Regardless of the source of tannin, microbial fermentation was inhibited in both grains, as demonstrated by a decline in gas production, DM disappearance, volatile fatty acids and ammonia production. However, these effects were more pronounced for wheat than corn grain, mostly during the initial stages of the incubation. Scanning electron microscopy revealed that both sources of tannins inhibited the microbial hydrolysis of the endosperm protein matrix. Tannins did not prevent bacterial attachment to starch granules, but starch hydrolysis was slowed indirectly as a result of a tannin‐mediated reduction in the degradation of the surrounding protein matrix. Tannins are likely to be more effective at modulating the rate of starch digestion in grains that possess a readily degradable protein matrix. Copyright © 2006 Society of Chemical Industry
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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 | 0.000 | 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) | 0.001 | 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".