Optimization of α‐amylase production in solid substrate fermentation
Bibliographic record
Abstract
Abstract Response surface methodology (RSM) was used to optimize the medium components of α‐amylase production using solid substrate fermentation (SSF). Hazelnut cake (HC), peptone, yeast extract (YE), and (NH4)2SO4 were selected as independent variables for optimization. Central composite design (CCD) was used in design experiments and analysis results. This procedure limited the number of actual experiments performed while allowing possible interactions between the independent variables. By using CCD, 30 experiments were performed for determining the interaction of independent variables and optimization of fermentation medium. The P‐value of the coefficient of linear effect of (NH4)2SO4 concentrations, which was obtained as 0.0001 has shown that this parameter has the greatest effect on the production of α‐amylase. Model F‐value (5.62) implies that the model is significant. The highest α‐amylase activity (4895 IU) was measured when the HC, peptone, YE, and (NH4)2SO4 concentrations in the medium were 22.62, 5.20, 1.62, and 6.81 g L−1, respectively.
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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) | 0.002 | 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 | 0.001 | 0.001 |
| 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".