A single HPLC‐PAD‐APCI/MS method for the quantitative comparison of phenolic compounds found in leaf, stem, root and fruit extracts of <i>Vaccinium angustifolium</i>
Bibliographic record
Abstract
A method was developed for the analysis of Vaccinium angustifolium Ait. (Lowbush blueberry), which is a widely used natural health product, particularly for the treatment of diabetic symptoms. While the anthocyanin content of the fruit has been well characterized, the chemistry of the vegetative parts used in supportive therapy for diabetes has been largely ignored. Using a metabolomics-based approach for compound identification with an emphasis on phenolic metabolites, a single HPLC-PAD-APCI/ MS method was developed for the separation and quantitation of the major metabolites found in the 95% ethanol extracts of leaf, stem, root and fruit. The leaf extract contained high concentrations of chlorogenic acid (approximately 100 microg/mg extract) and a variety of quercetin glycosides that were also detected in the fruit and stem extracts. Flavan-3-ol monomers (+)-catechin and (-)-epicatechin were found in all plant parts but their procyanidin dimers were exclusively identified in the stem and root. The accuracy and precision of the presented method were corroborated by low intra- and inter-day variations in quantitative results in all plant part extracts. Further validation of the extraction and analytical protocols focused on identified compounds with reputed anti-diabetic activity, revealing recoveries greater than 80% and detection limits of 0.12-2.73 microg/mL.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".