Comparative studies of univariate and multivariate optimizations for manganese determination in antihypertensive drugs by electrothermal atomic absorption spectrometry
Bibliographic record
Abstract
Comparative studies using the univariate and multivariate optimizations (factorial design) were undertaken to determine manganese in antihypertensive drugs by electrothermal atomic absorption spectrometry (ET AAS). For the univariate method, the optimum pyrolysis and atomization temperatures were 500 and 2200 °C, and for multivariate analysis, the temperatures were 500 and 2400 °C, respectively. No differences were observed between the methods with respect to the slopes of the calibration curves, recoveries, results for certified urine samples for the two levels, and the averages of the intra- and inter-assay coefficients of variation (CV) using Student t and F tests as a statistical tool (P < 0.05). Calibration was performed by matrix matching. All other studies were made using the optimized multivariate conditions. The characteristic mass was of 0.68 ± 0.17 pg (the recommended mass is 0.60 pg). The limits of detection and quantification were 0.23 and 0.77 µg L -1 , respectively. Intra- and inter-assay studies on the drug spiked with 0.5, 1.0, and 1.5 µg L -1 of Mn yielded mean results of 3.6 ± 1.9% and 9.6 ± 2.2%, respectively. For the intra-assay evaluation, seven samples of each concentration were evaluated for Mn on the same day, whereas, for the inter-assay study, these solutions were analyzed in three replicates during three consecutive days. Recovery studies on the drug spiked with three levels of Mn (n = 7 for each level) furnished results between 101.4 ± 17.2% and 106.6 ± 7.5%. The results of a certified urine sample analysis (two levels of Mn) agreed at a 95% level of confidence. Forty eight antihy pertensive drug samples were analyzed, and the drug contents varied between 2.9 ng and 1.9 µg capsule -1 .Key words: manganese, antihypertensive drugs, electrothermal atomic absorption spectrometry, multivariate optimization.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".