Characterisation of Phenolics in Flor‐Essence®—a Compound Herbal product and its Contributing Herbs
Bibliographic record
Abstract
INTRODUCTION: Commercially available herbal mixture FE, a proprietary natural health product manufactured by Flora Manufacturing and Distributing Ltd (Flora), is a unique North American traditional herbal product. FE is a chemically complex mixture of eight herbs and has not been subjected to phytochemical analysis. OBJECTIVE: To develop analytical methods to undertake detailed phytochemical analyses of FE, and its eight contributing herbs, including burdock (Arctium lappa L.), sheep sorrel (Rumex acetosella L.), Turkish rhubarb (Rheum palmatum L.), slippery elm Muhl. (Ulmus rubra), watercress (Nasturtium officinale R. Br.), red clover (Trifolium pratense L.), blessed thistle (Cnicus benedictus L.) and kelp (Laminaria digitata Lmx.). METHODOLOGY: The identification was undertaken by a combination of reversed phase high performance liquid chromatography-diode array detection-atmospheric pressure chemical ionisation-mass selective detection (RP-HPLC-DAD-APCI-MSD) analysis and phenolics metabolomic library matching. RESULTS: New separation methods facilitated the identification of 43 markers in the individual herbs which constitute FE. Sixteen markers could be identified in FE originating from four contributing herbs including four caffeoyl quinic acids, three dicaffeoyl quinic acids and two caffeic acid derivatives from A. lappa, luteolin-7-O-glucoside, luteolin, five apigenin glycosides and apigenin from R. acetocella and N. officinale and sissostrin from T. pretense. A validated method for quantitative determination of three markers is reported with good intraday, interday and interoperator repeatability using a reliable alcohol based extraction technique. CONCLUSION: FE and its contributing herbs predominantly contain phenolics. This methodology can be applied to further develop full-scale validation of this product.
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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 | 0.001 |
| 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".