Roselle Calyces Particle Size Effect on the Physicochemical and Phytochemicals Characteristics
Bibliographic record
Abstract
The effect of average particle size (APS), type of solvent, and extraction times (ET) on the physicochemical (moisture, pH, total soluble solids (TSS), titratable acidity, color, water activity (aw), density), and phytochemical (total anthocyanins and phenols content)propertiesin Hibiscus sabdariffa (Roselle) calyces was analyzed. The phytochemical properties evaluation was performed using a factorial design 2×3×3: two APS (median diameters, d50, of 0.55 ± 0.016 (fine powder) and 0.97 ± 0.034 (ground powder) mm), three solvents (water, 2% citric acid, and 50% ethanol) and three ET (30, 45, and 60 min). All extractions were performed at 50 °C. The APS was determined by sieve analysis using Tyler sieves of different number of mesh. Regarding physicochemical properties, no significant differences (p > 0.05) were observed in moisture content, pH, and titratable acidity; however, the 0.55 mm fine powder (FP) of Hibiscus calyces had lower aw(0.37±0.01) and higher TSS (5.53±0.05%) than the 0.97 mm ground powder (GP). The extracts obtained fromGP showed a deeper red color than those of FP. The best combination of independent variables, in order to obtain the highest concentration of anthocyanins (451.4±28.1 mg/100 g d.s.) and total phenols (2016.2 ± 159.8 mg/100 g d.s.) were APS of 0.55 mm, 50% ethanol, and ETof 30 min.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".