Multi‐enzymatic recovery of fungal cellulases ( <scp> <i>Aspergillus niger</i> </scp> ) through solid‐state fermentation of sugarcane bagasse
Bibliographic record
Abstract
Abstract In this study, the optimum conditions for the multi‐enzymatic recovery of cellulases produced by Aspergillus niger were investigated using sugarcane bagasse. Two extraction methods were investigated: the two‐stage solid–liquid extraction (SLE) followed by ultrasound‐assisted extraction (UAE), and the single‐stage SLE. The ultrasound effects were evaluated using a Doehlert design, in which the pH (5.0–9.0) and sonication power (0.8–2.0 W ml −1 ) were independent variables. For the single‐stage SLE, temperature (25–45°C), time (10–60 min), and pH (5.0–9.0) were analyzed using the Box–Behnken design. Both processes were monitored to evaluate FPase, CMCase, and β‐glucosidase (U ml −1 ) activities. The maximum enzymatic activities (EA) obtained for FPase, CMCase, and β‐glucosidase in the SLE–UAE were 0.352, 0.321, and 1.412 U ml −1 , respectively. Unlike in most previous studies, sonication was insignificant ( p < 0.05) with respect to the enzymatic complex within the evaluated ranges. Moreover, sonication changed the EA when lower than 1.2 and higher than 1.6 W ml −1 , mainly inhibiting the EA of β‐glucosidase. The single‐stage SLE was more effective than the two‐stage SLE–UAE, and the maximum EA values for FPase, CMCase, and β‐glucosidase were 0.354, 0.303, and 3.135 U ml −1 , respectively. The single‐stage process was better because it consumed less energy, required simpler equipment, and provided higher efficiency in a shorter time. This study will improve diversified enzyme extraction from sugarcane bagasse, reduce enzyme production costs, and enhance bagasse utilization.
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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".