Metrologically traceable quantification of trifluoroacetic acid content in peptide reference materials by<sup>19</sup>F solid-state NMR
Bibliographic record
Abstract
Although solution-state NMR is frequently used in metrologically-traceable quantification studies, this is not the case for solid-state NMR. However, solid-state NMR allows quantification of substances without the need of dissolution, providing a truly non-destructive approach, and extending metrologically-traceable quantitative NMR to sample classes that are difficult to characterize in solution. In this contribution we present a thorough and rigorous protocol for ¹⁹F quantitative solid-state NMR employing a certified reference material as external calibrant to provide metrological traceability to absolutely quantify the content of trifluoroacetic acid (TFA) in a peptide sample, typically the major impurity in synthetic peptides. The protocol includes determining the quantitative volume of the solid-state NMR sample holder (rotor), the ERETIC (electronic reference to access in vivo concentrations) method (Akoka et al 1999 Anal. Chem. 71 2554) to compensate for variations in the sensitivity of the radio frequency resonant circuit when an external calibrant is used, and the EASY (elimination of artefacts in NMR spectroscopy) method (Jaeger and Hemmann 2014 Solid State Nucl. Magn. Reson. 57–58 22) to effectively suppress the ¹⁹F NMR background signal from the probe head. We applied the protocol to quantify the amount of TFA in a candidate NRC certified reference material of the peptide angiotensin II. The results obtained by ¹⁹F quantitative solid-state NMR are in excellent agreement with those obtained by quantitative NMR in solution employing an internal calibrant.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".