Optimization of the autoclave-assisted alkaline delignification of cocoa (Theobroma cacao) pod husks using KOH to maximize reducing sugars
Bibliographic record
Abstract
Cocoa pod husks are a type of biomass that is still poorly explored. This work was carried out as part of the optimization of the delignification process for this residue with potassium hydroxide, to maximize the reducing sugars content. Screening for potentially influencing factors showed that the biomass to solvent ratio and the temperature had the greatest effect on the delignification process. Optimization of these factors using a composite central plan revealed that the quadratic model was the most suitable for predicting the rate of delignification. The predicted R² (0.815) was in good agreement with the adjusted R² (0.906). The correlation coefficient (R2 = 0.945) between the predicted and experimental results confirmed the fit of the model. The optimal conditions were a biomass to solvent ratio of 9.14% and a temperature of 128 °C, which resulted in a maximum degree of delignification of 93.9%, with 80% of the solids recovered. This study found that the removal of extractables before the pretreatment considerably improved the delignification of cocoa pod husks and the production of reducing sugars, which increased from 3.15 ± 0.006 mg/mL to 5.33 ± 0.143 mg/mL. Scanning electron microscopy and X-ray diffraction confirmed physicochemical changes in the biomass after pretreatment.
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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) | 0.000 | 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".