Multi-response and multi-criteria optimization of acid hydrolyzate detoxification of cocoa pod husks: Effect on the content of phenolic compounds and fermentable sugars
Bibliographic record
Abstract
Dilute acid hydrolysis is the most common and effective method for converting lignocellulosic substrates into fermentable sugars. However, this hydrolysis partially degrades the lignin into phenolic compounds (PC), inhibiting the fermentation medium by retaining it in the hydrolyzate. Response surface methodology is a modeling and optimization technique used to examine the effect of multiple factors on a given response. In this study, shows the removal of PC from cocoa pod husks hydrolyzate, while preserving a considerable level of reducing sugar (RS). An Alkalinization from pH 11 with NaOH, then readjustment of pH to 6 with H 2 SO 4 were first carried out, while eliminating 89.39% of PC and 13.41% of sugars. Then, an optimization of the activated carbon detoxification of the hydrolyzate was carried out by considering the contact time factors (X 1 ), carbon to hydrolyzate ratio (X 2 ) and the agitation speed (X 3 ) in a Box-Behnken plan. The optimal conditions were 60 min of contact, a carbon to hydrolyzate ratio of 1.984% (w/v), and a stirring speed of 180 revolutions per minute (rpm). 0.153 mg/mL of PC and 6.585 mg/mL of RS remained in the hydrolyzate, corresponding to 95.18% of PC and 28.88% of RS lost.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".