Total Phenol, Flavanoid and Antioxidant Activity of Physalis angulata Leaves Extract by Subcritical Water Extraction
Bibliographic record
Abstract
Physalis angulata, having familiar name in Indonesia as “Ceplukan”, is wellknown empirically in folk medicineto treat several diseases such as hepatitis, malaria, boil, liver problem, diuretic etc. Clinically several researchershave revealed the activity of Physalis angulata extract as anticancer, antitumor, antimycobacterial,immunosuppresion etc. So far, the common method to obtain Physalis angulata extract is by hot waterextraction (HWE) and maceration using organic solvent such as methanol or ethanol. Meanwhile, strickerregulation of organic solvent residue to the pharmaceutical product encourages the research to replace organicsolvent by environmentally benign solvent. The objective of this research is to investigate the potential ofPhysalis angulata leaves extract obtained by Subcritical Water Extraction (SWE) method as antioxidantThe Physalis angulata leaves were extracted by water in subcritical condition. Water in this condition may havepolarity similar with organic solvent, so it can extract the phytochemical in plant material. Three variables wereinvestigated including pressure (100-200 bar), temperature (100-250oC) and extraction time (15-45 min). Afterevaporating the water, the extracts were analyzed for antioxidant activity, total phenol and flavanoid usingspectrophotometer. Water content in extract was analyzed by karl fischer titrator. The result revealed thatpressure has negligible effect, while temperature has significant effect to the antioxidant activity, total phenol andflavanoid content. The results also compared with that of obtained by conventional methods such as maceration(water and ethanol), HWE and soxhlet.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".