A surrogate signal model for automated 1D 1H NMR compound quantification
Bibliographic record
Abstract
Bioreactors are useful tools for bioprocessing and production of biologics, gene therapies and vaccines. Streaming data-driven process control systems can be valuable in lowering the cost of production or discovering novel reaction pathways. Nuclear Magnetic resonance (NMR) is an inexpensive spectroscopy technique that has characteristics that make it appropriate for on-line, high-throughput measurement of metabolic changes in a bioreactor vessel. Future quantitative NMR (qNMR) advancements for processing this type of streaming data could grant a unique possibility for in-situ bioprocessing applications. One significant challenge for 1D 1H qNMR is that the spectrum of a compound can deviate from its spectrum in a reference setting, especially across the various spectrometer frequency and concentration profile of metabolite mixture in the biofluid sample. A robust predictive or constraint model on the generative mechanism of the measured NMR signal can help guide future qNMR developments. We present an approximated 1D 1H NMR signal model that shows promise in fitting chemical shifts and other interpretable parameters for small mixtures of compounds. Our model use reference chemistry parameters of compounds to derive patterns between its nuclei via spin Hamiltonian simulations and hierarchical convex clustering on a spin angular momentum feature between the nuclei, which are quantum subsystems. These patterns are used to construct a surrogate model of the compound mixture with a lower degrees-of-freedom. Our approach does not require any phase or baseline correction techniques to pre-process the data, making it a generative model that fully accounts for the relative phase information, which is usually attenuated in a heuristic manner and ignored in conventional NMR data processing. We demonstrate the potential of this new methodology by fitting against real-world NMR reference compound experiments.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".