Pelargonium graveolens: Towards In-Depth Metabolite Profiling, Antioxidant and Enzyme-Inhibitory Potential
Bibliographic record
Abstract
Pelargonium graveolens L’Hèr. (Geraniaceae) is renowned for its traditional use as a flavor, ornamental and medicinal plant. This work aimed at an in-depth study of the phytochemical profiling and in vitro antioxidant and enzyme inhibition assessment of a methanol-aqueous extract from P. graveolens leaves. A UHPLC-HRMS analysis revealed more than 110 secondary metabolites, including 8 acyltartaric and 11 acylcitric/acylisocitric acids; 8 gallotannins; 36 flavonols, flavanones and methoxylated flavonoids together with 17 phenolic and aliphatic acids; and 21 phenolic acid glycosides. For the first time, acylcitric acids along with feruloyl- and coumaroyltartaric acids are reported in the species. The leaf extract actively scavenged 2,2-diphenyl-1-picrylhydrazyl DPPH (273.45 mg trolox equivalent (TE/g)) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS•+) radicals (531.97 mgTE/g) and showed a high reducing power: 431.32 mg TE/g Cupric reducing antioxidant capacity (CUPRAC) and 292.21 mg TE/g Ferric reducing antioxidant power (FRAP). It possessed a metal chelating capacity (13.44 ethylenediaminetetraacetic acid equivalent (EDTAE)/g) and contained 2.71 mmol TE/g in the phosphomolybdenum assay. The rose geranium extract exhibited high inhibition towards acetyl- and butyrylcholinesterase (2.80 and 2.20 mg galantamine equivalent (GALAE)/g, respectively) and tyrosinase (75.49 mg kojic acid equivalent (KAE)/g). It inhibited α-glucosidase and α-amylase (3.75 mmol and 0.79 acarbose equivalent (ACAE)/g, respectively) and lipase (28.91 mg orlistat equivalent (OE)/g). This study sheds light into the future potential application of the rose geranium in pharmaceutical and nutraceutical products.
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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 | 0.000 |
| 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".