Effects of Extraction Techniques on Total Phenolic Content and Antioxidant Capacities of Two Oregano Leaves
Bibliographic record
Abstract
The effects of dried oregano leaves (Mediterranean and Mexican oregano) extracted using different extraction techniques, solvent types, and six different ratios of each solvent to distilled water on total phenolic (TP) content and antioxidant properties were examined. The Folin-Ciocalteu and 1,1-Diphenyl-2-picryl-hydrazyl (DPPH) assays were performed to assess the antioxidant capacity. The different species of oregano had a significant effect on TP content (107.6 vs. 34.5 mg GAEg-1 in Mexican vs. Mediterranean oregano, respectively) (P<0.05). Comparing extraction techniques, the vortex procedure significantly increased the measured TP content compared to sonication or shaking (P<0.05); however, its effectiveness was sample species and solvent type dependent. Solvent type also had a significant impact on TP content of extracts in decreasing order of acetone, methanol, ethanol, and water (P<0.05). The solvent:water ratio on TP content of each extract was significant (P<0.05); higher TP content was measured for 40:60 and 60:40 acetone:water ratios for Mediterranean and 60:40 and 80:20 acetone:water ratios for Mexican oregano. The antioxidant capacity had a strong relationship with total phenolic contents. The current findings indicated that the species, extraction techniques, solvent type and the ratio of solvent:water had a significance influence on the TP content of two different species of dried oregano leaf, which may be a possible reason behind most variability reported on TP compounds of herbal and medicinal plants.
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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) | 0.001 | 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 | 0.001 |
| 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".