Production of Ethanol from Ripe Plantain Peel Hydrolysate by Saccharomyces cerevisiae
Bibliographic record
Abstract
Aim: Nigeria is amongst the largest Musa paradisiaca (Plantain) producing countries and ripe plantain peels are discarded as waste thus polluting the environment. Utilization of this agricultural waste to useful products like ethanol will be a welcome development. The influence of pretreatment on plantain peels hydrolysate for ethanol production by Saccharomyces cerevisiae and the effect of media supplementation were studied.Methodology: The pretreatment methods used before carrying out fermentation of the hydrolysate were acid, steam and alkali. Parameters analyzed in all the hydrolysates and during fermentation were cell number, pH value, ethanol, glycerol concentration and inhibitory compounds using standard procedures.Results: The results showed that acid pretreatment had the highest cell number, glycerol and ethanol concentrations of 27.30 ± 2.47 x 106 cells/mL, 4.43± 0.15 mg/mL and 12.31± 0.08 mg/mL respectively. Alkali pretreated plantain peel hydrolysate had the least values of 12.25 ± 1.77 x 106 cells/mL, 3.81 ± 0.10 mg/mL and 7.50 ± 0.21 mg/mL for cell number, glycerol and ethanol concentrations, respectively. There was a significant difference in ethanol production when comparing the acid pretreatment to the others (P < 0.05). The acid hydrolysate was optimized by supplementing the media and results showed that the cell number, glycerol and ethanol concentration slightly increased.Conclusion: It was observed that acid hydrolysate of plantain peels can be utilized economically as a cheap substrate for bioethanol production and the yield can be enhanced through media supplementation.
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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 | 0.000 | 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".