Flavonoid Content of Phaleria macrocarpa Fruit and Its Proximate Compositions
Bibliographic record
Abstract
Flavonoids are one of the compounds in phenolic compounds in fruits. Flavonoids have been documented to modulate or modify lipid peroxidation, free radical scavenging activity, and inhibition of hydrolytic and oxidative enzymes. Flavonoids also influence anti-inflammatory action, anti-tumour, anti-hyperglycemia, anti-viral, anti-microbial, and anti-fungal effects. In this research, flavonoid content in P. macrocarpa fruits was determined, as well as its proximate compositions. To extract flavonoids in the fruit, P. macrocarpa fruits were extracted by the Soxhlet extraction method using aqueous as a solvent. Total flavonoid content in P. macrocarpa fruit extract was 89.89 ± 3.71 mg QE/100 mL. Proximate analyses were conducted to determine the fruit’s moisture content, ash content, dry matter, crude protein, crude fibre, and crude essential oil. Results obtained for proximate composition were 9.45 ± 2.67% (crude protein), 21.633 ± 1.17 (fibre), and 5.605 ± 0.88 (essential oil). Moisture content in this fruit was 88.401 ± 0.749%, the dry matter was 10.96%, and the ash content was 6.33 ± 3.72%. FTIR analysis shows the extract’s functional spectra of phenol, alkane, alkene, and alkyne groups.
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 | 0.001 | 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) | 0.002 | 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".