Quantitative analysis of sitagliptin using the<sup>19</sup>F-NMR method: a universal technique for fluorinated compound detection
Bibliographic record
Abstract
To expand the application scope of nuclear magnetic resonance (NMR) technology in quantitative analysis of pharmaceutical ingredients, (19)F nuclear magnetic resonance ((19)F-NMR) spectroscopy has been employed as a simple, rapid, and reproducible approach for the detection of a fluorine-containing model drug, sitagliptin phosphate monohydrate (STG). ciprofloxacin (Cipro) has been used as the internal standard (IS). Influential factors, including the relaxation delay time (d1) and pulse angle, impacting the accuracy and precision of spectral data are systematically optimized. Method validation has been carried out in terms of precision and intermediate precision, linearity, limit of detection (LOD) and limit of quantification (LOQ), robustness, and stability. To validate the reliability and feasibility of the (19)F-NMR technology in quantitative analysis of pharmaceutical analytes, the assay result has been compared with that of (1)H-NMR. The statistical F-test and student t-test at 95% confidence level indicate that there is no significant difference between these two methods. Due to the advantages of (19)F-NMR, such as higher resolution and suitability for biological samples, it can be used as a universal technology for the quantitative analysis of other fluorine-containing pharmaceuticals and analytes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".