Optimization of Fermentation Medium for Producing α-Hydroxyphenylacetic Acid by Using Plackett-Burman Design and Response Surface Methodology
Bibliographic record
Abstract
Plackett-Burman design and response surface methodology were applied in order to optimize the fermentation medium of (R)-α-hydroxyphenylacetic acid ((R)-HPA) producing Bacillus sp. HZG-19. The factors playing important roles in the production of (R)-HPA were selected based on Plackett-Burman design. The path of steepest ascent was undertaken to optimize said fermentation medium. Finally, the optimal levels of the factors with the greatest change in regard to product yield were further optimized using Box-Behnken and response surface analysis. The optimal conditions were found to be as follows: casein peptone 30.49 (g × L-1), glycerol 14.09 (g × L-1), KH2PO4 0.1345 (g × L-1), K2HPO4 0.01 (g × L-1), CaCl2 0.1 (g × L-1), MnSO4 0.01 (g × L-1). Under the optimal conditions described above, the yield of (R)-HPA reached 63.30%, which indicated an increase of 14.9%, as compared to the yield obtained before optimization.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".