Quantification of Chlorogenic Acid and Hyperoside Directly from Crude Blueberry (Vaccinium angustifolium) Leaf Extract by NMR Spectroscopy Analysis: Single-Laboratory Validation
Bibliographic record
Abstract
A single-laboratory-validated NMR spectroscopy method was established for determining the quantity of chlorogenic acid and hyperoside from crude extract material of blueberry leaves of the species Vaccinium angustifolium var. laevifolium House. The calibration curve of chlorogenic acid showed a highly linear regression, R = 0.99998. NMR spectroscopy identification and quantification of the constituents directly from the mixture, within the error of HPLC-diode array detector analysis, were determined as 7.53 mM chlorogenic acid (64.0 mg chlorogenic acid/g powdered leaf) and 0.77 mM hyperoside (8.58 mg hyperoside/g powdered leaf). The LOD was calculated to be 0.01 mM and the LOQ 0.01 mM by the 9 min and 13 s NMR spectroscopy experiment utilized. The assay showed no significant interference from different field strengths, extraction mesh size, gravimetric scale precision, NMR spectroscopy tube type, pulse program, amount of starting dry material, or day-to-day operation. The robustness of NMR spectroscopy as a means of definitively monitoring chlorogenic acid and hyperoside content directly from crude extracts was demonstrated by Youden statistical analysis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".