Microbe Selection and Optimizing Process Parameters for Degradation of Glucosinolates in Rapeseed Meal by Box-Behnken Design
Bibliographic record
Abstract
The present study applied Aspergillus oryzae, Aspergillus niger, Penicillium purpurogenum, Trichoderma sp. MAB-2010b and Saccharomyces cerevisiae in solid state fermentation to degrade the glucosinolates in rapeseed meal. In addition, SDS-PAGE was used to determine the effect of hydrolysis of those five microbes on peptide size in rapeseed meal. The results indicated that the solid state fermentation with S. cerevisiae degraded the glucosinolates more than those with other microbes. The peptides were hydrolyzed by S. cerevisiae to a greater extent than others. Thus the following procedure was just focused on the solid state fermentation with S. cerevisiae. Box-Behnken design of response surface methodology was applied to optimize the substrate to water ratio, inoculum amount, and duration. The glucosinolate level in rapeseed meal was as the response. The optimal conditions derived from response surface methodology for S. cerevisiae fermentation were: 1.0 of substrate to water ratio, 1.5 mL (equal to 5%) of inoculum amount, and 48 h of duration. The minimum content of glucosinolates was 0.46 μmol/g dry matter. S. cerevisiae used in the present study thus exhibit the potential use in large scale solid state fermentation for increasing nutrition quality of rapeseed meal.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".