Curcuminoids in Turmeric Roots and Supplements: Method Optimization and Validation
Bibliographic record
Abstract
Curcuma longa L. rhizomes are used extensively as a spice in food preparations and dietary supplements for their anti-inflammatory and antioxidant properties. An expert review panel (ERP) evaluated analytical methods for the quantitation of individual curcuminoids for the purpose of identifying a method for official method status. It was requested that several modifications be undertaken to improve method performance prior to subjecting the chosen method to a single-laboratory validation. Two separate Plackett-Burman factorial studies were used to identify factors that contributed to the chromatographic separation and extraction of curcuminoids. Significant factors were further optimized to produce the improved HPLC method for curcuminoid separation. This method was then subjected to a single-laboratory validation according to the AOAC International guidelines for linearity, detection limits, precision, and accuracy. The two most significant factors impacting the quantitation of curcuminoids were column temperature and extraction solvent, which were optimized to 55 °C and 100 % methanol, respectively. The validation was performed on 12 raw materials and finished products containing turmeric roots. The method precision was reported using HorRat values which were within recommended ranges of the AOAC guidelines. Overall accuracy of the method was accessed at three separate levels for each analyte and ranged from 99.3–100.9 %. The validated method is suitable for quantitation of individual curcuminoids in turmeric raw materials and finished products and is recommended for consideration as an official method by the AOAC International.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".