Optimization of Ultrasonic-Assisted Extraction Process of Polysaccharides from American Ginseng and Evaluation of Its Immunostimulating Activity
Bibliographic record
Abstract
Ultrasonic-assisted extraction (UAE) of American ginseng polysaccharides (AGP) was investigated using response surface methodology. Three-factor-three-level Box-Behnken design was employed to optimize the ultrasonic power, extraction time and ratio of water to raw material to obtain a high AGP yield. The analysis of variance and response surface plots indicated that ultrasonic power was the most important factor affecting the extraction yield. The optimal conditions were ultrasonic power 400 W, extraction time 71 min, and ratio of water to raw material 33 mL g−1. Under these conditions, the yield of AGP was 8.09%, which was agreed closely to the predicted value. Gas chromatography (GC) analysis showed that AGP was composed of arabinose, rhamnose, galactose, glucose, and galacturonic acid. Fourier transform infrared spectra revealed the general characteristic absorption peaks of AGP. In addition, AGP exhibited good immunostimulating activities by up-regulating the production of nitric oxide and cytokines. Compared with hot water extraction, UAE required shorter extraction time and gave a higher extraction yield, without changing the structure and immunostimulating activity of AGP. The results indicated that UAE could be an effective and advisable technique for the large scale production of plant polysaccharides.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".