Insights into Nuclear Magnetic Resonance Data Preprocessing: A Comprehensive Review
Bibliographic record
Abstract
Nuclear magnetic resonance (NMR) and its derivatives play a pivotal role in molecular analysis across research and clinical domains. However, the intricate nature of NMR data preprocessing, which is integral for accurate analysis, is not easily understood despite the availability of numerous software tools. This comprehensive review aims to unravel the complexities of preprocessing algorithms in both the time and frequency domains. It covers essential steps such as direct current offset removal, eddy current correction, shift and linear prediction, weighting, zero filling, domain transformation, phase error correction, baseline correction, solvent filtering, calibration and alignment, reference deconvolution, binning/bucketing, peak picking, peak fitting/deconvolution, compound identification, integration and quantification, normalization, and transformation. The review uses plain language to enhance accessibility and understanding. By demystifying the algorithms behind these preprocessing steps, we seek to help researchers and practitioners in navigating the nuances of NMR data preprocessing, ultimately fostering better understanding and practical application in molecular analysis. Received:1 February 2024| Revised: 20 May 2024| Accepted: 30 May 2024 Conflicts of Interest Aixiang Jiang is an Editorial Board Member for Journal of Data Science and Intelligent Systems and was not involved in the editorial review or the decision to publish this article. The author declares that she has no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Aixiang Jiang: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".