Advancing quantitative NMR for high-precision isotopic analysis with rnmrfit 2.0
Bibliographic record
Abstract
Quantitative NMR is widely utilized in isotopic ratio measurement for determining the origins and authenticity of chemical compounds. Achieving high precision required for such analyses depends on accurately separating signal from noise, which is essential for reliable quantification of resonance peak areas. In this study, we present rnmrfit 2.0, an NMR peak-fitting tool tailored for high precision isotopic analysis. This new version incorporates semi-global peak fitting with automated peak region selection, achieving greater robustness and computational efficiency than previously reported. The newly developed software was used to explore the impact of two common spectral processing techniques, line broadening and zero filling, as well as the choice of baseline span on peak fitting precision. All three were found to have a significant impact on fit precision, with optimal settings for line broadening and zero filling deviating from what is commonly recommended for 13C spectra, at 1-3 Hz and 0.5-1.0, respectively. Compared to commercial tools including TopSpin and MestReNova, rnmrfit demonstrated superior precision and trueness, achieving precision as low as 0.26% for 2H and 0.16% for 13C. The new version of rnmrfit is available as an open-source executable, offering a scalable solution for isotopic analysis with minimal user input, paving the way for more reliable isotopic quantification.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.019 |
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".