Curcuminoids in Turmeric Roots and Supplements: Method Optimization and Validation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it