Insights into Nuclear Magnetic Resonance Data Preprocessing: A Comprehensive Review

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.

Keywords

nuclear magnetic resonance / preprocessing / time domain / frequency domain

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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