Improvement of Quality and Digestibility of Moringa Oleifera Leaves Feed via Solid-State Fermentation by Aspergillus Niger
Bibliographic record
Abstract
Abstract The Moringa oleifera leaf is an important source worldwide with a high nutritional value and functions in food and feed that may also treat a myriad of ailments but the leaf has low organoleptic properties and digestibility. To overcome this shortcoming, a novel Aspergillus niger was isolated from the Moringa leaf material. The fungal strain grows well on moist Moringa leaves and requires no additives. After performing a single factor test for temperature, moisture, inoculation size, and fermentation, the optimized condition was determined by using a response surface method, followed by a small-scale production test. The pleasant, sweet smelling aroma in the fermented leaves was then generated, supplementing than its native repulsive smell. The protein content and digestibility of the leaves increased by 23.4 % and 54.4 %, respectively; the direct-fed microbes reached up to 1.99 × 109 CFU per gram of fermented freeze-dried Moringa leaves. Digestive lignocellulolytic enzymes were substantially produced with 2.97 ± 0.24 U.g−1 of filter paper activity and 564.9 ± 37.4 U.g−1 of xylanase activity. Moreover, some functional components, such as flavonoids and γ-Aminobutyric acid content, were also significantly increased compared to that of the unfermented leaves. In conclusion, the feed quality and digestibility of Moringa oleifera leaves were greatly improved via solid-state fermentation by Aspergillus niger. Fermented Moringa oleifera can be used as a potentially high- quality feed alternative for the animal 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".