Chitinolytic and Chitosanolytic Activities from Crude Cellulase Extract Produced by A. niger Grown on Apple Pomace Through Koji Fermentation
Bibliographic record
Abstract
Enzyme extracts of cellulase [filter paper cellulase (FPase) and carboxymethyl cellulase (CMCase)], chitinase, and chitosanase produced by Aspergillus niger NRRL-567 were evaluated. The interactive effects of initial moisture and different inducers for FP cellulase and CMCase production were optimized using response surface methodology. Higher enzyme activities [FPase 79.24+/- 4.22 IU/gram fermented substrate (gfs) and CMCase 124.04+/-7.78 IU/gfs] were achieved after 48 h fermentation in solid-state medium containing apple pomace supplemented with rice husk [1% (w/w)] under optimized conditions [pH 4.5, moisture 55% (v/w), and inducers veratryl alcohol (2 mM/kg), copper sulfate (1.5 mM/kg), and lactose 2% (w/w)] (p<0.05). Koji fermentation in trays was carried out and higher enzyme activities (FPase 96.67+/-4.18 IU/gfs and CMCase 146.50+/-11.92 IU/gfs) were achieved. The nonspecific chitinase and chitosanase activities of cellulase enzyme extract were analyzed using chitin and chitosan substrates with different physicochemical characteristics, such as degree of deacetylation, molecular weight, and viscosity. Higher chitinase and chitosanase activities of 70.28+/-3.34 IU/gfs and 60.18+/-3.82 to 64.20+/-4.12 IU/gfs, respectively, were achieved. Moreover, the enzyme was stable and retained 92-94% activity even after one month. Cellulase enzyme extract obtained from A. niger with chitinolytic and chitosanolytic activities could be potentially used for making low-molecular-weight chitin and chitosan oligomers, having promising applications in biomedicine, pharmaceuticals, food, and agricultural industries, and in biocontrol formulations.
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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 | 0.000 | 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".