Single-Lab Validation for Determination of Kavalactones and Flavokavains in Piper methysticum (Kava)
Bibliographic record
Abstract
(Kava) is a plant whose roots are used in the preparation of traditional beverages with spiritual, medicinal, and social importance for the Pacific Islanders. Kava is also sold as a herbal supplement or recreational beverage consumed for its mild inebriating effect in Europe and North America. With an ongoing interest in the safety and quality of kava products, it is necessary to develop a validated method for determination of kava chemical composition to ensure confidence in quality assessment. Thus, an high-performance liquid chromatography with ultraviolet detection (HPLC-UV) method was developed, optimized, and validated for determining six major kavalactones and three flavokavains in kava raw materials and finished products based on AOAC single-laboratory validation guidelines. This is the first fully validated analytical method for measuring kavalactones and flavokavains in a single run. The separation of the analytes was achieved in 10 min with an Agilent Poroshell C18 column using gradient separation. The sample was extracted with methanol first and then acetone. The signals were detected at 240 nm and 355 nm. The limit of quantification was under 1.2 µg/mL (0.3 mg/g) for kavalactones and under 0.35 µg/mL (0.01 mg/g) for flavokavains. The Horwitz ratio values described ranged from 0.3 to 1.82. The spike recovery experiments showed an accuracy between 92 and 105% for all analytes. The results of the study demonstrate that the method is fit for the purpose of determining methysticin, dihydromethysticin, kavain, dihydrokavain, yangonin, desmethoxyyangonin, flavokavain A, flavokavain B, and flavokavain C in kava raw material and finished products (dry-filled capsule, liquid phytocaps, and tincture).
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".