Determination of Aloin A and Aloin B in <i>Aloe vera</i> Raw Materials and Finished Products by High-Performance Liquid Chromatography: Single-Laboratory Validation
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Bibliographic record
Abstract
A single-laboratory validation (SLV) was conducted on an HPLC method for the detection and quantification of aloin A and aloin B in Aloe vera raw materials and finished products. An extraction procedure using sonication with an acidified solvent was used for solid test materials while liquid test materials only required dilution, if necessary, prior to filtration and analysis. Separation was achieved using a fused core C18 column in 18 min under isocratic elution conditions allowing for a single analyte (aloin A) calibration curve to quantify both aloins. Adequate chromatographic resolution (Rs ≥ 1) was achieved for aloin A and aloin B. The calibration curves for aloin A exhibited coefficients of determination (r(2)) of ≥ 99.9% over the linear range of 0.3-50 μg/mL. The LOD values were 0.092 and 0.087 μg/mL, and LOQ 0.23 and 0.21 μg/mL for aloin A and aloin B, respectively. Repeatability studies were performed on nine test materials on each of 3 separate days, with five of the test materials determined to be above the LOQ having repeatability RSD (RSDr) values ranging from 0.61 to 6.30%. Method accuracy was determined through a spike recovery study on both liquid and solid matrixes at three different levels: low, medium, and high. For both aloins, the recovery in the liquid matrix ranged from 92.7 to 106.3% with an RSDr of 0.15 to 4.30%, while for the solid matrix, the recovery ranged from 84.4 to 108.9% with an RSDr of 0.23 to 3.84%. Based on the results of the SLV study, it is recommended that this method be evaluated for reproducibility through a collaborative study.
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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 | 0.000 |
| 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