Lignocellulosic Processing with Acid Pretreatment and Enzymatic Hydrolysis for Improving the Acquisition of Sugar Fermentation
Bibliographic record
Abstract
Banana peels is a waste which has not been widely used, so that the relatively long time the existence of such waste brings its own problems such as pollution. Banana peels contain high enough lignocellulosic and can be degraded into simpler forms. The Lignin content of banana peels needs to be removed / destroyed structure. The purpose of this study was to determine the effect of the addition ratio of Trichoderma reseei and Aspergillus niger on hydrolysis liquefaction and saccharification time in stage hydrolysis to glucose produced with the highest glucose parameters. In this study, the method was used to degrade lignin by using sulfuric acid (2%). Afterward, the cellulose content was changed in liquefaction process. Hydrolysis liquefaction performed using Aspergillus niger and Trichoderma reesei with a mixture ratio of 1: 0, 0: 1, 1: 1 and 1: 2. Furthermore, substrate hydrolysis liquefaction saccharification going through the process of hydrolysis to increase glucose levels were formed. Hydrolysis liquefaction aims to change the content of starch in the banana skin into glucose using Saccharomyces cerevisiae as the addition of 20% (v / v). Hydrolysis process of liquefaction is done by varying the sampling time on day - 3, 6, and 9. In this study the hydrolysis of liquefaction of banana peels use a mixture of Trichoderma reesei and Aspergillus niger 2:1 at a temperature of 50oC, pH 5, and 64 h for resulted in glucose with the best content was 0.52%. In addition, the results showed that the hydrolysis saccharification with the addition of Saccharomyces cerevisiae glucose levels were highest on day 3 with a temperature of 30oC and pH 5, resulting in glucose content to 1.63%.
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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 | 0.001 |
| 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".