Kitchen Waste Residues asKitchen Waste Residues as Potential Renewable Biomass Resources for the Production of Multiple Fungal Carbohydrases and Second Generation Bioethanol
Bibliographic record
Abstract
Utilization of kitchen waste, the major portion of municipal solid waste for the coproduction of multiple carbohydrases and bioethanol was investigated in this study. Solid-state fermentation was performed to evaluate the potential of various steam pretreated kitchen waste residues as substrates for the coproduction of cellulolytic, hemicellulolytic, pectinolytic, amylolytic enzymes by a locally isolated strain of Aspergillus niger CJ-5. All the kitchen waste residues simply moistened with water, without the supplementation of exogenous nutrients proved good for the induction of all the enzyme components of a cocktail after 96 h incubation. Of all the substrates evaluated, steam pretreated potato peels induced maximum yields corresponding to 69.0±1.92U CMCase, 16.5±0.54U FPase, 44.0±1.28U β-glucosidase, 999.0±28.90U xylanase, 58.2±2.12U mannanase, 120.0±3.72U pectinase, 31520.0±375.78U α-amylase, 482.8±9.82U glucoamylase/g dry substrate (gds). Saccharification of residues using inhouse produced crude enzyme cocktail resulted in the release of 610±10.56, 570±8.89, 435±6.54, 475±4.56, 445±4.27, 385±4.49, 370±6.89, 490±10.45 mg of total reducing sugars/g of dried potato peels, orange peels, pineapple peels, mausami peels, onion peels, banana stalks, pea pods and composite mixture respectively revealing carbohydrate conversion efficiencies in the range of 97.0-99.4%. After fermentation of released hexoses, alcohol yields ranging from 80±1.069 - 262±7.86 µL/gds were obtained.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".