Version 1
: Received: 3 November 2023 / Approved: 9 November 2023 / Online: 9 November 2023 (14:22:18 CET)
How to cite:
Jiang, A. Mathematical and Statistical Review of NMR Frequency Domain Data Pre-processing. Preprints2023, 2023110644. https://doi.org/10.20944/preprints202311.0644.v1
Jiang, A. Mathematical and Statistical Review of NMR Frequency Domain Data Pre-processing. Preprints 2023, 2023110644. https://doi.org/10.20944/preprints202311.0644.v1
Jiang, A. Mathematical and Statistical Review of NMR Frequency Domain Data Pre-processing. Preprints2023, 2023110644. https://doi.org/10.20944/preprints202311.0644.v1
APA Style
Jiang, A. (2023). Mathematical and Statistical Review of NMR Frequency Domain Data Pre-processing. Preprints. https://doi.org/10.20944/preprints202311.0644.v1
Chicago/Turabian Style
Jiang, A. 2023 "Mathematical and Statistical Review of NMR Frequency Domain Data Pre-processing" Preprints. https://doi.org/10.20944/preprints202311.0644.v1
Abstract
Magnetic Resonance Imaging (MRI) is widely used in clinics and research due to its accurate disease identification and non-invasive nature. MRI is based on Nuclear Magnetic Resonance (NMR), which is also extensively employed in various fields. NMR and its modern versions involve intricate pre-processing steps before data analysis. These steps are initially processed in the time domain and subsequently in the frequency domain. While our previous review focused on time domain pre-processing (https://www.preprints.org/manuscript/202310.2032/v1), this review delves into the mathematical and statistical aspects of frequency domain pre-processing. We discuss essential pre-processing steps like phase error correction, baseline correction, solvent filtering, calibration and alignment, reference deconvolution, binning/bucketing and peak picking, peak fitting/deconvolution and compound identification, integration and quantification, normalization and transformation. Furthermore, we offer practical recommendations for each step.
Keywords
NMR pre-processing; phase error correction; baseline correction; reference deconvolution; integration and quantification; normalization and transformation
Subject
Computer Science and Mathematics, Signal Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
