Qualitative and Quantitative Phenolic Compounds Analysis of Dicranopteris linearis Different Fractional Polarities Leaves Extract
Bibliographic record
Abstract
Dicranopteris linearis occupies in an open ground that contains poor soils and often colonizing disturbed space that partly shaded area. It has been known for various traditional values including medicinal, edible food, soil erosion protection, pen and furniture. Even though the plants possess both economic and medicinal value, they still form the neglected group of a plant. The present study was carried out to characterize the phenolic compounds in D. linearis leaves extract in different fractional polarities qualitative and quantitatively. Dried leaves of D. linearis were successfully extracted by using water extraction before separated by petroleum ether, ethyl acetate and butanol fractions. All the fractional extracts have been analysed by using GCTOF-MS and HPLC. The result from GCTOF-MS analysis of fractional extracts showed 38 compounds found in petroleum ether, ethyl acetate and butanol extracts. However, only four phenolic compounds were identified through HPLC analysis in ethyl acetate and butanol extracts which were 2-Methoxy-4-vinylphenol, Vanillin, 4-Hydroxybenzaldehyde and 4-Hydroxybenzohydrazide. The results revealed that D. linearis contained 699.83 ± 6.26 μg GAE /g DW of total phenolic acid whereas individual phenolic acids were predominantly caffeic acid (0.44 ± 0.01 μg/g DW) and ferulic acid (0.22 ± 0.00 μg/g DW) in ethyl acetate and caffeic acid (0.10 ± 0.00 μg/g DW) and 2-Coumaric acid (0.44 ± 0.00 μg/g DW) in butanol extracts. In the present study, the plant extracts demonstrated the highest phenolic compound detected in ethyl acetate and butanol compared to petroleum ether extract.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".