Determination of bioactive nonvolatile ginger constituents in dietary supplements by a rapid and economic HPLC method: Analytical method development and single-laboratory validation
Bibliographic record
Abstract
Most of the validated methods for ginger-containing dietary supplements have long run time and low sensitivity and only analyze gingerols and shogaols. 6-Paradol and zingerone become popular in modern dietary supplement industry as bioactive ginger constituents. Therefore, we developed an efficient HPLC-UV/Vis method to analyze all above major constituents. Compared to 282/280 nm used by the current compendial United States Pharmacopeia (USP) monograph method and International Organization for Standardization (ISO) 13685-1997 method, detection wavelength was optimized to 230 nm which showed a higher sensitivity (signal-to-noise ratio) and better peak resolution. For measuring the ginger constituents in AOAC required matrices, the method was demonstrated to be selective, linear (R 2 > 0.999), specific, accurate (91.1–103.2% spike recovery rate) and precise (RSD r < 5%, RSD R < 8%). Among 10 commercial ginger-containing samples that we screened using this method, the results were 80–123% of the products’ labeling value. The HPLC running time was successfully shortened from 29 min (USP method) and 40 min (ISO method) to 12 min without the need of using an expensive Mass Spectrometer for analyte separation. The method is the first method that meets all AOAC SMPR 2017.12 requirements and therefore has the potential to be adopted as a consensus industrial reference method for meeting FDA's cGMP Compliance for the manufacture and quality control of dietary supplements and ingredients.
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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 | 0.008 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".