Improvement of Animal Feed Additives of Ginkgo Leaves through Solid-state Fermentation using <i>Aspergillus niger</i>
Bibliographic record
Abstract
To improve the quality of Ginkgo biloba leaves as biological feed additives, twelve Aspergillus niger strains were evaluated for their growth in the moisture ginkgo leaf meal media through solid-state fermentation. The results relating to flavor, flavonoids, enzymes, crude protein, and reducing sugars showed A. niger Gyx086 strain was capable of efficiently fermenting ginkgo leaves. The optimal cultural conditions were three loops of spores inoculation to every 75 g medium containing 60 % water, grew at 28C for 48 h. The Gyx086 grew well in the medium. The fermented leaves generated a strong sweet-smelling odor, could be identified by electronic nose equipment using a cluster analysis, other than the original offensive smell from non-fermented ginkgo leaves. Each gram dried culture with Gyx086 showed 2.83 10 9 CFU of A. niger; 3.19 0.37 FPU of acid-resistant filter paper activity. Its total contents of flavonoids, reducing sugars, and crude proteins were 19.95 0.23 mg, 24.28 2.35 mg, and 162.81 3.46 mg in each gram of leaves, 26.03 %, 62.73 %, and 14.58 % higher than the controls, respectively. The essential amino acids and total amino acids contents were 96.41 % and 16.49 % higher than the controls.
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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) | 0.001 | 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".