Enhanced fumaric acid production from brewery wastewater by immobilization technique
Bibliographic record
Abstract
Abstract BACKGROUND Enhanced fumaric acid production by immobilization of filamentous fungal strains on different solid supports has been reported previously. However, the carbon‐rich agro‐industrial waste biomass ‘brewery wastewater’ as fermentation medium and muslin cloth as immobilizing device for the fungal strain Rhizopus oryzae 1526 have never been investigated before. In the present research work, enhanced production of fumaric acid by an immobilization technique was carried out with a novel combination of fermentation medium, immobilization device and fungal strain. RESULTS Muslin cloth area of 25 cm 2 and 1.5 × 10 6 per mL spore concentration were found optimal for the highest production of fumaric acid. Production level and volumetric productivity of fumaric acid were markedly increased from 30.56 ± 1.40 to 43.67 ± 0.32 g L −1 and 0.424 to 1.21 g L −1 h for immobilized submerged fermentation compared with free‐cell fermentation, respectively. However, the specific fumaric acid production rates for free‐cell and 25 cm 2 muslin cloths were found to be comparable (3.39 and 3.49 gg −1 h −1 , respectively). Scanning electron microscope studies of the immobilized fungus confirmed the good attachment of the fungal hyphae to the muslin cloths. CONCLUSION Results demonstrated that brewery wastewater and muslin cloth could be used for the enhanced production of fumaric acid through submerged fermentation. © 2014 Society of Chemical Industry
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".