Purification, composition and antioxidant activity of polysaccharides from wolfberry, cherry, kiwi and cranberry fruits
Bibliographic record
Abstract
Water-soluble polysaccharides from wolfberry (Lycium barbarum L.), sweet cherry (Prunus avium L.), kiwi (Actinidia chinensis L.) and cranberry fruits (Vaccinium macrocarpon Aiton) were extracted with boiling water, fractionated using ion exchange column chromatography, and characterized for molecular weight by high performance size exclusion chromatography (HPSEC).Monomer sugar composition was determined by gas chromatography (GC), and antioxidant activity was assayed by oxygen radical absorbance capacity (ORAC) and Trolox equivalent antioxidant capacity (TEAC).All four types of fruit investigated had four separate polysaccharide fractions; however, the polysaccharides from sweet cherries had higher molecular weight fractions.All the fruits contained rhamnose, fucose, arabinose, xylose, mannose, galactose, and glucose, but the polysaccharides from different fruits, and from cherries of different cultivars and maturity levels, had different ratios of simple sugars.TEAC and ORAC assays revealed that raw and purified polysaccharides from cherries, cranberries, kiwi, and wolfberries have antioxidant activity, and sweet cherry polysaccharides have the highest antioxidant activity.
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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 | 0.001 | 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 | 0.000 |
| 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".