A RP‐HPLC‐DAD‐APCI/MSD method for the characterisation of medicinal Ericaceae used by the Eeyou Istchee Cree First Nations
Bibliographic record
Abstract
INTRODUCTION: Ericaceae medicinal plants are traditionally used by the Eeyou Istchee Cree and other northern peoples of North America to treat type 2 diabetic symptoms. Because of the importance of phenolics as potential cures for degenerative diseases including type 2 diabetes, an analytical method was developed to detect them in the leaf extracts of 14 Ericaceae plants. OBJECTIVE: To develop an optimised method which is applicable to a relatively large number of Ericaceae plants using their leaf extracts. For this purpose phenolics with a wide range of polarity, including a glucosylated benzoquinone, two phenolic acids, three flavanols, a flavanone, a flavone and five flavonols, were included in this study. METHODOLOGY: Characterisation of phytochemicals in extracts was undertaken by automated matching to the UV spectra to those of an in house library of plant secondary metabolites and the authentication of their identity was achieved by reversed phase-high-performance chromatography-diode array detection-atmospheric pressure chemical ionisation/mass selective detection. RESULTS: Twenty-six phenolics were characterised within 26 min of chromatographic separation in 80% ethanol extracts of 14 Ericaceae plants. The calibration curves were linear within 0.5-880 microg/g dry mass of the plant with regression values better than 0.995. The limits of detection ranged from 0.3 for microg/mL for (+)-catechin to 2.6 microg/mL for chlorogenic acid. This is a first study dealing with relatively large number of Ericaceae extracts and is applicable to other plants of same family.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".