Phenolic Profile and Antioxidant Capacity of Invasive Solidago canadensis L.: Potential Applications in Phytopharmacy
Bibliographic record
Abstract
Canadian goldenrod (Solidago canadensis L.), an invasive plant in Europe, is known for its allelopathic activity and is rich in bioactive compounds like flavonoids and phenolic acids, with significant pharmacological potential. This study presents the LC-MS phenolic profiles of leaf and flower extracts from S. canadensis, an invasive alien plant in the Istria region (Croatia). Total phenolics (TP) (45.78–110.68 mg GAE/g DW) and non-flavonoids (TNF) (28.38–72.20 mg GAE/g DW) were found to be more abundant in ethanolic than in methanolic extracts. The antioxidant capacity (AC), as measured by ABTS, DPPH, and FRAP assays, was higher in flower extracts compared to leaf extracts. A non-targeted metabolomics approach was used, and 41 phenolic compounds in leaves and 36 in flowers were identified, with hydroxycinnamic acids and flavonols being the most abundant. 5-caffeoylquinic acid was quantitatively predominant in the leaf extracts, while quercetin-3-rutinoside dominated the flower extracts. Five leaf-specific compounds were identified (dicaffeoylquinic acid 2, 4-p-coumaroylquinic acid 1, p-hydroxybenzoic acid, quercetin-3-rhamnoside, and quercetin acetylhexoside 1), suggesting targeted extraction for different pharmacological applications. This study highlights the therapeutic potential of S. canadensis and underscores the need for further research on the bioavailability, efficacy, and safety of its compounds, potentially transforming this ecological threat into a valuable resource for drug development.
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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) | 0.000 | 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) | 0.001 | 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".