Determination of Mitragynine in <i>Mitragyna speciosa</i> Raw Materials and Finished Products by Liquid Chromatography with UV Detection: Single-Laboratory Validation
Bibliographic record
Abstract
Mitragyna speciosa (kratom) is a tree indigenous to Southeast Asia, and its leaves are used in herbal formulations because they contain indole alkaloids mitragynine and 7-hydroxy (7-OH) mitragynine. An HPLC method was developed, optimized, and validated using single-laboratory validation guidelines to quantify mitragynine in kratom raw materials and finished products. The method optimization evaluated several extraction parameters including solvent type, solvent volume, time, and extraction method. The separation of the mitragynine alkaloids was achieved in 18 min with a fused-core C18 EVO column using gradient separation with ammonium bicarbonate (pH 9.5) and acetonitrile. The calibration range for mitragynine was 1.0-500 μg/mL with correlation coefficients of ≥99.9% throughout method development and validation. The method detection limit and LOQ were 0.2 and 0.6 μg/mL, respectively for mitragynine. Eight test samples were obtained to evaluate method repeatability. RSDr ranged from 0.4 to 1.0%, whereas intermediate precision ranged from 3.7 to 7.3%, with HorRat values from 0.68 to 1.96. 7-OH mitragynine was below the LOQ for all samples, therefore, spikes repeatability sample RSD values were <1%. The validation data presented meet the Standard Method Performance Requirements as specified by the AOAC INTERNATIONAL Kratom Working Group.
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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 | 0.001 |
| 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 | 0.001 | 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".