Successive grinding and sieving as a new tool to fractionate polyphenols and antioxidants of plants powders: Application to <i>Boscia senegalensis</i> seeds, <i>Dichrostachys glomerata</i> fruits, and <i>Hibiscus sabdariffa</i> calyx powders
Bibliographic record
Abstract
Abstract The present investigation aimed at evaluating the effect of powder fractionation based on particle size on the chemical composition in macronutrients, polyphenol contents, and antioxidant properties of powders of Boscia senegalensis seeds, Dichrostachys glomerata fruits, and Hibiscus sabdariffa calyces. Significant differences (p < 0.05) among granulometric classes of each plant were observed for the chemical composition in macronutrients. A decrease in particle size of plant powders was associated with an increase in ash, protein, and fat contents, while carbohydrate content was lowered. The following Granulometric classes, [0–180 µm] for Boscia senegalensis, [180–212 µm] for Dichrostachys glomerata, and [212–315 µm] for Hibiscus sabdariffa, respectively, were found to maximize total phenolic content and antioxidant activity. These results confirm that the grinding and controlled differential screening technology is an approach may serve as a useful guide to obtain optimum polyphenol extraction and enhance antioxidant activity of plant products.
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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 | 0.000 |
| 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".