Optimization of the Alcoholic Concentration Obtained From Sugary Cassava (Manihot esculenta Crantz) by Response Surface Methodology
Bibliographic record
Abstract
Sugary cassava or mandiocaba is a cassava variety of potential use for bioethanol production. In this study, laboratory-scale fermentations were carried out in a bioreactor with a working volume of 1L, using the yeast strain LNF CAT-1. A central composite design (CCD) was applied to determine the extent to which pH, temperature, and yeast concentration influence ethanol production with the aim of improving the fermentation process. The individual effects and the interaction of these factors were analyzed using a surface response method. Physicochemical properties of the material were also investigated and the analysis of root characterization showed high moisture content (~91%) and a low amount of starch (~4.0%), ash values close to 1.0%, total fibers 0.4%, proteins 0.15%, and lipids 0.1%. The results obtained from the wort presented a low acidity (~0.2%), pH close to neutrality (~6.5%), total soluble solids values of ~5.8%, glucose content ~2.3%, fructose ~1.0%, and sucrose ~1.2%. The second-order polynomial regression model determined that the maximum ethanol production of 2.8% (v/v) would be obtained when the optimum pH, temperature, and yeast concentration were ~5.0, 32-36 ºC, and ~10-14 g L-1, respectively.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".