Optimization of Water Based-extraction Methods for the Preparation of Bioactive-rich Ginger Extract Using Response Surface Methodology
Bibliographic record
Abstract
Aims: Evaluation of three extraction methods to prepare bioactive-rich ginger extract for incorporation into a functional beverage. Study Design: Response surface methodology. Methodology: For the preparation of bioactive-rich ginger extract with water, conventional hot water extraction, ultrasonic-assisted extraction and high pressure homogenization-assisted extraction were evaluated. Response surface methodology was employed to optimize the extraction conditions of each method with respect to the highest polyphenols, antioxidant capacity (ferric reducing antioxidant power; FRAP) and percent inhibition of low density lipoprotein (LDL) cholesterol oxidation. Results: Multiple response optimizations revealed that the optimum extraction conditions for each extraction method were 60min extraction time under 55°C for hot water extraction, 15min ultrasonication under 52°C for ultrasonic-assisted extraction and 62°C under 140MPa homogenization pressure for high pressure homogenization-assisted extraction. Conclusion: The extract prepared from the ultrasonic-assisted extraction method exhibited the highest polyphenol recovery and antioxidant activity, compared to the extracts prepared from other two methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".