Optimization of laccase production from Trametes versicolor by solid fermentation
Bibliographic record
Abstract
The regulation of culture conditions, especially the optimization of substrate constituents, is crucial for laccase production by solid fermentation. To develop an inexpensive optimized substrate formulation to produce high-activity laccase, a uniform design formulation experiment was devised. The solid fermentation of Trametes versicolor was performed with natural aeration, natural substrate pH (about 6.5), environmental humidity of 60% and two different temperature stages (at 37 degrees C for 3 days, and then at 30 degrees C for the next 17 days). From the experiment, a regression equation for laccase activity, in the form of a second-degree polynomial model, was constructed using multivariate regression analysis and solved with unconstrained optimization programming. The optimized substrate formulation for laccase production was then calculated. Tween 80 was found to have a negative effect on laccase production in solid fermentation; the optimized solid substrate formulation was 10.8% glucose, 27.7% wheat bran, 9.0% (NH4)2SO4, and 52.5% water. In a scaled-up verification of solid fermentation at a 10 kg scale, laccase activity from T. versicolor in the optimized substrate formulation reached 110.9 IU/g of dry mass.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".