Reverse Curve Fitting Approach for Quantitative Deconvolution of Closely Overlapping Triplets in Fourier Transform Nuclear Magnetic Resonance Spectroscopy Using Odd-Order Derivatives
Bibliographic record
Abstract
A new deconvolution strategy, reverse curve fitting, was developed to determine peak positions and independent intensities of overlapping Fourier transform (FT) nuclear magnetic resonance (NMR) bands. From the third-order derivative of the overlapping band, the peak position was estimated from its zero-crossing point and the peak intensity was quantitated by partial curve matching with its primary maxima. Every matched peak in the overlapping band was dismembered in turn to weaken the overlap until an independent peak was filtered out. The deconvolution can be refined progressively by manually tuning the peak positions and peak widths. In a simulation study, a closely overlapped 13C NMR triplet (overlapping degrees between 0.5 and 1.0) at a signal-to-noise ratio (SNR) of 20:1 was quantitatively deconvoluted by our reverse curve fitting procedure with a routine denoising technique. The noise interference and denoising technique were also studied in the simulation. A real FT-NMR overlapping band of Ethylbenzene (300 MHz) was satisfactorily deconvoluted and compatible with higher resolution literature spectral data. A more complicated overlapping NMR band of Tetraphenyl porphyrin was studied as well. This new approach to the deconvolutions is applicable to other FT spectroscopies.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".