Isolation and optimization of the fermentation condition of cellulolytic microbial isolates from cassava waste water
Bibliographic record
Abstract
The study was conducted to isolate and identify cellulose producing microorganisms from cassava waste water. Cassava waste water sample was obtained from a cassava processing factory at Lokogoma town in Wushishi Local government area of Niger State. The microorganisms were isolated, identified and counted by standard microbiological methods. The mean bacteria count ranges from 6.8 x 103 cfu/mL-1 to 2.1 x 103 cfu/mL-1 while the fungi count ranges from 3.2 x 103 cfu/mL-1 to 1.2 x 103 cfu/mL-1. A total of eight (8) bacterial; Staphylococcus aureus, Bacillus anthrax, Bacillus subtilis, Escherichia coli, Klebsilla sp, Bacillus megaterus, Staph. Epidermidis and Pseudomonas aeruginosa, and six (6) fungi; Saccharomyce serivicea, Aspergillus niger, Penecillium sp., Muccor sp., Aspergilus flavus and Aspergilus fumigetus isolates were identified in the waste water. Among these organism, the best cellulase activity was recorded for Bacillus subtilis (10.39 x 10-4 mg/ml/sec) and Aspergillus niger (11.21 x 10-4 mg/ml/sec). However, maximum activity was obtained at pH ranges from 3 ~ 9, temperature ranges from 30 oC ~ 80oC and substrate concentrations ranges from 1.5% ~3.0%. In conclusion, cassava processing water regarded as waste water could be an alternative source of microorganisms capable of producing cellulase enzyme for industrial purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".