Innovative method for improving fermentability of hemicellulosic hydrolysate by ozonation for sustainable xylitol production
Bibliographic record
Abstract
Biomanufacturing xylitol, a sugar alcohol from surplus rice straw (RS) offers an opportunity for its green production alongside fostering a sustainable bioeconomy. However, RS pretreatment for hemicellulose extraction (majorly xylose), creates several toxic compounds as side products that impede microbial metabolism. So, the yeast mediated production of xylitol is a big challenge due to low fermentability of RS hydrolysate. The present work explored the efficacy of ozone and hydrogen peroxide, O 3 -H 2 O 2 (O-HP), in integration with activated charcoal (AC) for detoxifying and decolorizing RS hydrolysate. The effect on fermentability of RS hydrolysate was studied with the yeast, M. caribbica CP02 for xylitol production. O-HP/AC process was able to remove ∼88.86 % of the aromatics (furan and phenolic compounds) and color (ΔE = 46.2) without substantial xylose loss, which is a key drawback of conventional detoxification treatments. In 3 L batch bioreactor study, O-HP treated hydrolysate produced 43.14 ± 0.84 gL −1 xylitol titre and 0.74 gg −1 yield after 72 h of fermentation, comparable to semi-synthetic medium (44.6 ± 0.70 gL −1 , 0.77 gg −1 ). Simultaneously, a xylitol yield of 0.69 gg −1 and a productivity of 0.56 gL −1 h −1 at 72 h was observed in a 14 L bioreactor. Conclusively, the O-HP process is a promising intervention that can be used as a significant step during detoxification/decolorization for bioprocessing of xylitol. • Significant reduction in total phenols, furans, and color with O 3 -H 2 O 2 process. • No significant loss in xylose level in O-HP treated hydrolysate as compared to control. • Improved fermentability and xylitol yield of O 3 -H 2 O 2 treated hydrolysate. • CCD and ANN predicted responses for O 3 -H 2 O 2 process optimization. • Xylitol yield of 0.69 gg −1 and productivity of 0.56 gL −1 h −1 observed in 14 L bioreactor.
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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) | 0.000 | 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 | 0.001 |
| 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".