Benchtop nuclear magnetic resonance performance evaluation according to ASTM E691–22 on a population of instruments: Molar substitution determination in hydroxypropyl betadex as a case study for use in quality control environments
Bibliographic record
Abstract
The inclusion of quantitative nuclear magnetic resonance (qNMR) spectroscopy in industry has historically been stifled by a lack of accessibility, caused in-part by the large costs of traditional high-field spectrometers, the maintenance required for these, and the expertise necessary to manage and use them. In recent years, the emergence of benchtop NMR technology, an accessible, affordable, and automatable alternative, has led to a more feasible incorporation of NMR into quality control spaces, an area traditionally reserved for other techniques such as gas chromatography and liquid chromatography, which are routinely combined with detection techniques such as mass spectrometry. While these techniques are commonly used in analyzer-type applications using gold standard methods of analysis, wherein an instrument is dedicated to performing specific assays, this remains uncommon for NMR. Herein, we perform a full method verification using benchtop qNMR on a population of benchtop NMR instruments according to the ASTM designation E691-22, a standard used to determine the precision of a test method. To our knowledge, this is the first published example of this type of study for benchtop NMR spectroscopy. For this work, a total of five analysts performed assays on 23 different benchtop NMR instruments for the analysis of hydroxypropyl betadex according to the USP-NF method, and the results are compared using a variety of statistical methods. The results of this work demonstrate that benchtop NMR technology is effective and robust under repeatability and reproducibility conditions and is a powerful tool for these types of routine quality control analyses.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".