1
|
Song P, Xu J, Liu X, Zhang Z, Rao X, Martinho RP, Bao Q, Liu C. Stationary wavelet denoising of solid-state NMR spectra using multiple similar measurements. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2024; 359:107615. [PMID: 38310668 DOI: 10.1016/j.jmr.2023.107615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 02/06/2024]
Abstract
Accumulating several scans of free induction decays is always needed to improve the signal-to-noise ratio of NMR spectra, especially for the low gyromagnetic ratio solid-state NMR. In this study, we present a new denoising approach based on the correlations between multiple similar NMR spectra. Contrary to the simple averaging of multiple scans or denoising the final averaged spectrum, we propose a Wavelet-based Denoising technique for Multiple Similar scans(WDMS). Firstly, the stationary wavelet transform is applied to decompose every spectrum into approximation coefficients and detail coefficients. Then, the detail coefficients are multiplied by weights calculated based on Pearson's correlation coefficient and structural similarity index between approximation coefficients of different spectra. Finally, the average of these detailed components is used to denoise the spectra. The proposed method is carried on the assumption that noise between multiple spectra is uncorrelated while peak signal information is similar between different spectra, thus preserving the possibility of applying further processing to the data. As a demonstration, the standard wavelet denoise is applied to the WDMS-processed spectra, achieving a further increase in the S/N ratio. We confirm the reliability of the denoising approach based on multiple scans on 1D/2D solid-state MAS/static NMR spectra. In addition, we also show that this method can be used to deal with a single Car-Purcell-Meiboom-Gill (CPMG) echo train.
Collapse
Affiliation(s)
- Peijun Song
- School of Science, Wuhan University of Technology, Wuhan 430070, China
| | - Jun Xu
- Key Laboratory of Magnetic Resonance in Biological Systems, Innovation Academy for Precision Measurement Science and Technology, Wuhan, 430071, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Xinjie Liu
- Key Laboratory of Magnetic Resonance in Biological Systems, Innovation Academy for Precision Measurement Science and Technology, Wuhan, 430071, PR China
| | - Zhi Zhang
- Key Laboratory of Magnetic Resonance in Biological Systems, Innovation Academy for Precision Measurement Science and Technology, Wuhan, 430071, PR China
| | - Xinglong Rao
- School of Science, Wuhan University of Technology, Wuhan 430070, China
| | - Ricardo P Martinho
- University of Twente Faculty of Science and Technology, Drienerlolaan 5, 7500AE Enschede, the Netherlands
| | - Qingjia Bao
- Key Laboratory of Magnetic Resonance in Biological Systems, Innovation Academy for Precision Measurement Science and Technology, Wuhan, 430071, PR China.
| | - Chaoyang Liu
- Key Laboratory of Magnetic Resonance in Biological Systems, Innovation Academy for Precision Measurement Science and Technology, Wuhan, 430071, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China; Optics Valley Laboratory, Hubei 430074, PR China.
| |
Collapse
|
2
|
Altenhof AR, Mason H, Schurko RW. DESPERATE: A Python library for processing and denoising NMR spectra. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2023; 346:107320. [PMID: 36470176 DOI: 10.1016/j.jmr.2022.107320] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/04/2022] [Accepted: 10/20/2022] [Indexed: 06/17/2023]
Abstract
NMR spectroscopy is an inherently insensitive technique with respect to the amount of observable signal. A common element in all NMR spectra is random thermal noise that is often characterized by a signal-to-noise ratio (SNR). SNR can be generically improved experimentally with repetitive signal averaging or during post-processing with apodization; the former of which often results in long experimental times and the latter results in the loss of spectral resolution. Denoising techniques can instead be used during post-processing to enhance SNR without compromising resolution. The most common approach relies on the singular-value decomposition (SVD) to discard noisy components of NMR data. SVD-based approaches work well, such as Cadzow and PCA, but are computationally expensive when used for large datasets that are often encountered in NMR (e.g., Carr-Purcell/Meiboom-Gill and nD datasets). Herein, we describe the implementation of a new wavelet transform (WT) routine for the fast and robust denoising of 1D and 2D NMR spectra. Several simulated and experimental datasets are denoised with both SVD-based Cadzow or PCA and WT's, and the resulting SNR enhancements and spectral uniformity are compared. WT denoising offers similar and improved denoising compared with SVD and operates faster by several orders-of-magnitude in some cases. All denoising and processing routines used in this work are included in a free and open-source Python library called DESPERATE.
Collapse
Affiliation(s)
- Adam R Altenhof
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL 32306, USA; National High Magnetic Field Laboratory, 1800 East Paul Dirac Drive, Tallahassee, FL 32310, USA
| | - Harris Mason
- Chemistry Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Robert W Schurko
- Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL 32306, USA; National High Magnetic Field Laboratory, 1800 East Paul Dirac Drive, Tallahassee, FL 32310, USA.
| |
Collapse
|
3
|
Long-term operation monitoring strategy for nuclear power plants based on continuous learning. ANN NUCL ENERGY 2022. [DOI: 10.1016/j.anucene.2022.109323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
4
|
Piacenza E, Chillura Martino DF, Cinquanta L, Conte P, Lo Meo P. Differentiation among dairy products by combination of fast field cycling NMR relaxometry data and chemometrics. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2022; 60:369-385. [PMID: 34632630 DOI: 10.1002/mrc.5226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 09/27/2021] [Accepted: 10/05/2021] [Indexed: 06/13/2023]
Abstract
A set of commercial milk and Sicilian cheeses was analysed by a combination of fast field cycling (FFC) nuclear magnetic resonance (NMR) relaxometry and chemometrics. The NMR dispersion (NMRD) curves were successfully analysed with a mathematical model applied on Parmigiano-Reggiano (PR) cheese. Regression parameters were led back to the molecular components of cheeses (water trapped in casein micelles, proteins and fats) and milk samples (water belonging to hydration shells around dispersed colloidal particles of different sizes and bulk water). The application of chemometric analysis on relaxometric data enabled differentiating milk from cheeses and revealing differences within the two sample groups of either cheeses or milk samples. Marked differences among cheeses were evidenced by statistical analysis of the sole quadrupolar peaks parameters, suggesting that these contain information on the nature of the milk used during cheese production. Hence, combination of FFC NMR and chemometrics represents a powerful tool to investigate alterations in dairy products.
Collapse
Affiliation(s)
- Elena Piacenza
- Department of Biological, Chemical and Pharmaceutical Sciences and Technologies, University of Palermo, Palermo, Italy
| | | | - Luciano Cinquanta
- Department of Agricultural, Food and Forest Sciences, University of Palermo, Palermo, Italy
| | - Pellegrino Conte
- Department of Agricultural, Food and Forest Sciences, University of Palermo, Palermo, Italy
| | - Paolo Lo Meo
- Department of Biological, Chemical and Pharmaceutical Sciences and Technologies, University of Palermo, Palermo, Italy
| |
Collapse
|
5
|
Altenhof AR, Jaroszewicz MJ, Frydman L, Schurko R. 3D Relaxation-Assisted Separation of Wideline Solid-State NMR Patterns for Achieving Site Resolution. Phys Chem Chem Phys 2022; 24:22792-22805. [DOI: 10.1039/d2cp00910b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
There are currently no methods for the acquisition of ultra-wideline (UW) solid-state NMR spectra under static conditions that enable reliable separation and resolution of overlapping powder patterns arising from magnetically...
Collapse
|