Fermentation of barley and wheat with lactic acid bacteria and exogenous enzyme on nutrient composition, microbial count, and fermentative characteristics
Bibliographic record
Abstract
Characteristics of wheat and barley inoculated with a homo-fermentative (HO) or hetero-fermentative (HE) lactic acid bacteria (LAB) were investigated in separate 97 d studies conducted using a 3 × 2 factorial arrangement comparing inoculants (no inoculant, HO or HE) and multi-enzyme (no or yes) addition. The pH declined (P < 0.05) to below 4.5 by day 6, coinciding with peaks in lactobacilli and yeast counts. A more rapid decline (P < 0.05) in pH and lactobacilli count but higher (P < 0.05) yeast count was observed with HO relative to HE. Enzyme addition reduced pH in inoculated grains only, particularly with HE (inoculant × enzyme effect; P < 0.05). Higher dry matter losses (P < 0.05) were observed with HE, most apparent in barley. Lactate was generally highest in HO and was increased by enzyme addition. Acetate was higher (P < 0.05) in HE. Ethanol and ammonia were lowest (P < 0.05) in HO. Wheat neutral detergent fibre (NDF) was reduced by both inoculants compared with control, whereas enzyme addition reduced NDF content in both grains. In conclusion, although not marked, fermentation responses appeared greater in wheat than barley although either LAB inoculant improved grain fermentation characteristics. The multi-enzyme appeared active during fermentation.
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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) | 0.001 | 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".