Improved xylanase production using apple pomace waste by <i><scp>A</scp>spergillus niger</i> in koji fermentation
Bibliographic record
Abstract
Xylanase production by Aspergillus niger NRRL‐567 in solid‐state fermentation (koji fermentation) was optimized using 24 factorial design and response surface methodology. The evaluated variables were the initial moisture level and concentration of inducers [veratryl alcohol (VA), copper sulphate (CS), and lactose (LAC)], leading to the response of xylanase production. Initial moisture level and LAC were found to be the most significant variable for xylanase production (p<0.05). The highest xylanase production was observed with 3578.8 ± 65.3 IU/gds (gram dry substrate) under optimal conditions using initial moisture of 85% (v/w), pH 5.0 and inducers VA (2 mM/kg), LAC 2% (w/w), and CS (1.5 mM/kg) after 48 h of incubation time. Higher xylanase activity of 3952 ± 78.3 IU/gds was attained during scale‐up of the process in solid‐state tray fermentation under optimum conditions after 72 h of incubation time. The present study demonstrates that A. niger NRRL‐567 can efficiently be used to achieve xylanase production with an economical and environmental benefit in solid‐state tray fermentation. The developed process can be used to develop an effective process for commercially feasible bioproduction of xylanases for speciality applications, such as conversion of lignocellulosic biomass to biofuels and other value‐added products.
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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) | 0.001 | 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 | 0.000 |
| 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".