Production of β‐glucosidase by <i>Aspergillus niger</i> <scp>CDBB‐H</scp> ‐175 on submerged fermentation
Bibliographic record
Abstract
Abstract The production of high‐activity β‐glucosidase at low cost is essential to increase the efficiency of cellulose hydrolysis, necessary for the economically feasible production of biofuels from the conversion of renewable lignocellulosic resources, specifically agricultural waste. In this work, the Aspergillus niger CDBB‐H‐175 strain was used for the production of extracellular β‐glucosidase. In the first stage of this work, the production of β‐glucosidase was carried out in a shake flask, using different carbon sources in order to evaluate the effect of the substrate on enzyme activity; in this way, it was determined that the best substrate is maltose, obtaining 2954 U/ml of β‐glucosidase activity at 31 days of culture. In the second stage, a laboratory‐scale study was done using two discontinuous bioreactor systems for submerged fermentation, stirred tank and airlift, using maltose, sucrose, and glucose as a carbon source. The results showed that β‐glucosidase with the highest enzymatic activity (3122 U/ml at 192 h of fermentation) was produced at uncontrolled pH conditions in an airlift bioreactor with maltose. In a third stage, using an airlift bioreactor with maltose, an orthogonal experimental design L 4 with three factors was applied: pH, aeration, and maltose concentration. The aeration was of utmost importance to guarantee a better enzymatic expression, and acidification of the culture medium during the fermentation process was another necessary condition for a greater enzymatic production.
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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 | 0.001 |
| 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".