Process Optimization of Deep Eutectic Solvent Pretreatment of Coffee Husk Biomass
Bibliographic record
Abstract
The increased processing of coffee beans has generated huge amount of coffee husk, which are improperly disposed. Inappropriate disposal of coffee husk has led to release of toxic compounds to the environment causing serious environmental concerns. To mitigate the impact of improperly disposed coffee husk, it is suggested for valorisation of the coffee husk. Hence, this study has focussed on identifying the potential of coffee husk in maximizing the sugar yield from it which can be converted to value added product. Deep eutectic solvent (DES) involving choline chloride and lactic acid (ChCl:LA) mixed at 1:4 molar ratio was studied to investigate the effect of DES pretreatment on coffee husk to produce reducing sugar in the hydrolysis process. Pretreatment conditions of the biomass were optimized for biomass loading (5-20%, w/w), temperature (70-120 °C), and duration (60-240 min) using Response Surface Methodology (RSM) for obtaining maximum yield of reducing sugar. The RSM model predicted an optimal pretreatment condition of biomass loading with 20% (w/w), pretreated at 120 °C for 231.80 min to achieve maximum sugar yield (30.522%). The pretreatment effect on biomass composition was analyzed using the Van Soest method, which showed an increase in the cellulose content along with the hemicellulose removal when compared with the native biomass. Moreover, evaluation of chemical structural changes also confirmed the effectiveness of DES pretreatment. Thus, the current study would illustrate the potential of coffee husk to produce value-added compounds from it.
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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 | 0.001 |
| 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".