Extracting flavonoid from <i>Ginkgo biloba</i> using lignocellulolytic bacteria <i>Paenarthrobacter sp</i>. and optimized via response surface methodology
Bibliographic record
Abstract
Abstract Ginkgo biloba flavonoids are important natural bioactive compounds with strong physiological effects. To develop an eco‐friendly and effective method for extracting flavonoids from plants, a microbial method involving lignocellulose‐degrading bacterial enzymes was developed to improve flavonoid extraction from Ginkgo biloba leaves. In this study, a newly isolated bacterial strain, Paenarthrobacter sp. S1.3, was employed for the fermentation processing of Ginkgo biloba leaf powder. The enzymatic characteristics of xylanase, CMCase, polygalacturonase, and β‐glucosidase from Paenarthrobacter sp. S1.3 were also investigated. The major fermentation parameters for the microbial extraction of Ginkgo biloba flavonoids were optimized using response surface methodology. Under optimal conditions (30.6 °C, initial pH 7.6, and fermentation time 25 h), a total flavonoid yield of 34.15 ± 0.52 mg g−1 dry weight was obtained, which was 2.11 fold and 1.42 fold higher than the yields obtained from the unfermented and ethanol extraction methods, respectively. The optimized results also suggest that the polygalacturonase and β‐glucosidase might play an important role in flavonoid extraction. Fermentation utilizing lignocellulose‐degrading bacteria was a feasible green approach to improve the extraction of natural compounds from plants. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd
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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".