Global Least-Squares Analysis of Large, Correlated Spectral Data Sets:  Application to Component-Resolved FT-PGSE NMR Spectroscopy

1996 ◽  
Vol 100 (20) ◽  
pp. 8180-8189 ◽  
Author(s):  
P. Stilbs ◽  
K. Paulsen ◽  
P. C. Griffiths
Keyword(s):  
Nmr Spectroscopy ◽  
Least Squares ◽  
Spectral Data ◽  
Data Sets ◽  
Pgse Nmr

Smooth Factor Analysis (SFA) to Effectively Remove High Levels of Noise from Spectral Data Sets

2017 ◽  
Vol 72 (5) ◽  
pp. 765-775 ◽  
Author(s):  
Yeonju Park ◽  
Isao Noda ◽  
Young Mee Jung
Keyword(s):  
Factor Analysis ◽  
Least Squares ◽  
Spectral Data ◽  
Partial Least Squares ◽  
Noise Level ◽  
Principal Component ◽  
Noise Removal ◽  
Correlation Spectroscopy ◽  
Data Sets ◽  
Spectral Data Analysis

Smooth factor analysis (SFA) is introduced as an effective method of removing heavy noise from spectral data sets. A modified form of the nonlinear iterative partial least squares (NIPALS) algorithm involving the smoothing of factors at each step is used in SFA. Compared with the conventional smoothing techniques for individual spectra, SFA is much more effective in the treatment of very noisy spectra (∼40% noise level). Smooth factor analysis invokes a large number of smooth factors to retain pertinent spectral information for high fidelity without distortion. This approach can be used as an effective general pretreatment procedure for multivariate spectral data analysis, such as principal component analysis (PCA) and partial least squares (PLS). This SFA method was also applied to the real experimental data, and its results successfully demonstrated the powerful potential for effective noise removal. Furthermore, this treatment is found to be very helpful to assist effective interpretation of two-dimensional correlation spectroscopy (2D-COS) spectra with very high noise level, which was not possible before.

scholarly journals Relationship between Li+ diffusion and ion conduction for single-crystal and powder garnet-type electrolytes studied by 7Li PGSE NMR spectroscopy

2019 ◽  
Vol 21 (42) ◽  
pp. 23589-23597 ◽  
Author(s):  
Kikuko Hayamizu ◽  
Yasuhiko Terada ◽  
Kunimitsu Kataoka ◽  
Junji Akimoto ◽  
Tomoyuki Haishi
Keyword(s):  
Nmr Spectroscopy ◽  
Ionic Conductivity ◽  
Single Crystal ◽  
Ion Conduction ◽  
Diffusion Constants ◽  
Pgse Nmr

Li-diffusion constants of single-crystal and powder garnets were determined and plotted versus ionic conductivity. Estimated NE carrier numbers were larger than atomic Li numbers for metal containing garnets and insensitive to temperature.

Migration‐based traveltime waveform inversion of 2-D simple structures: A synthetic example

Geophysics ◽  
2001 ◽  
Vol 66 (3) ◽  
pp. 845-860 ◽  
Author(s):  
François Clément ◽  
Guy Chavent ◽  
Susana Gómez
Keyword(s):  
Least Squares ◽  
Waveform Inversion ◽  
Domain Of Attraction ◽  
Optimization Methods ◽  
Data Sets ◽  
Seismic Reflection Data ◽  
Prestack Migration ◽  
Reflection Data ◽  
Full Wave ◽  
The Time Domain

Migration‐based traveltime (MBTT) formulation provides algorithms for automatically determining background velocities from full‐waveform surface seismic reflection data using local optimization methods. In particular, it addresses the difficulty of the nonconvexity of the least‐squares data misfit function. The method consists of parameterizing the reflectivity in the time domain through a migration step and providing a multiscale representation for the smooth background velocity. We present an implementation of the MBTT approach for a 2-D finite‐difference (FD) full‐wave acoustic model. Numerical analysis on a 2-D synthetic example shows the ability of the method to find much more reliable estimates of both long and short wavelengths of the velocity than the classical least‐squares approach, even when starting from very poor initial guesses. This enlargement of the domain of attraction for the global minima of the least‐squares misfit has a price: each evaluation of the new objective function requires, besides the usual FD full‐wave forward modeling, an additional full‐wave prestack migration. Hence, the FD implementation of the MBTT approach presented in this paper is expected to provide a useful tool for the inversion of data sets of moderate size.

Neural network modelling for very small spectral data sets: reduction of the spectra and hierarchical approach

2000 ◽  
Vol 54 (2) ◽  
pp. 93-106 ◽  
Author(s):  
Valeri Tchistiakov ◽  
Cyril Ruckebusch ◽  
Ludovic Duponchel ◽  
Jean-Pierre Huvenne ◽  
Pierre Legrand
Keyword(s):  
Neural Network ◽  
Spectral Data ◽  
Data Sets ◽  
Hierarchical Approach ◽  
Network Modelling ◽  
Neural Network Modelling

scholarly journals Regularized partial least squares with an application to NMR spectroscopy

2012 ◽  
Vol 6 (4) ◽  
pp. 302-314 ◽  
Author(s):  
Genevera I. Allen ◽  
Christine Peterson ◽  
Marina Vannucci ◽  
Mirjana Maletić-Savatić
Keyword(s):  
Nmr Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares

scholarly journals 1H-NMR spectroscopy identifies potential biomarkers in serum metabolomic signatures for early stage esophageal squamous cell carcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8151 ◽  
Author(s):  
Yan-Yan Liu ◽  
Zhong-Xian Yang ◽  
Li-Min Ma ◽  
Xu-Qing Wen ◽  
Huan-Lin Ji ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Discriminant Analysis ◽  
Nmr Spectroscopy ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Least Squares ◽  
Partial Least Squares ◽  
Early Stage ◽  
H Nmr ◽  
Potential Biomarkers

Background Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent types of upper gastrointestinal malignancies. Here, we used 1H nuclear magnetic resonance spectroscopy (1H-NMR) to identify potential serum biomarkers in patients with early stage ESCC. Methods Sixty-five serum samples from early stage ESCC patients (n = 25) and healthy controls (n = 40) were analysed using 1H-NMR spectroscopy. We distinguished between different metabolites through principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis (OPLS-DA) using SIMCA-P+ version 14.0 software. Receiver operating characteristic (ROC) analysis was conducted to verify potential biomarkers. Results Using OPLS-DA, 31 altered serum metabolites were successfully identified between the groups. Based on the area under the ROC curve (AUROC), and the biomarker panel with AUROC of 0.969, six serum metabolites (α-glucose, choline, glutamine, glutamate, valine, and dihydrothymine) were selected as potential biomarkers for early stage ESCC. Dihydrothymine particularly was selected as a new feasible biomarker associated with tumor occurrence. Conclusions 1H-NMR spectroscopy may be a useful tumour detection approach in identifying useful metabolic ESCC biomarkers for early diagnosis and in the exploration of the molecular pathogenesis of ESCC.

Darstellung neuer Phosphazene und ihre Reaktionen / Preparation of new Phosphazenes and their Reactions

1972 ◽  
Vol 27 (5) ◽  
pp. 486-491 ◽  
Author(s):  
Herbert W. Roesky ◽  
Wolfgang Kloker
Keyword(s):  
Nmr Spectroscopy ◽  
Spectral Data ◽  
Mass Spectral Data ◽  
Mass Spectral

The preparations of the following compounds are described: O = PF2N = PCl2N = PCl3, O = PF2N = PCl2N = PCl2N (CH3) 2, O = PF2N=PCl2N = PCl2N (C2H5) 2, O = PF2N = PCl2N (CH3) 2, O = PF2N = PCl2N (C2H5)2, O = PF2N = PCl2N (CH3) Si (CH3)3, O = PF2N = PCl2NCS, O = PFClN = PCl2N (CH3)2, O = PFClN = PCl2N (C2H5)2, O = PFClN = PCl [N (C2H5)2]2 and O =P (C6H5) FN = PCl3. They were characterized by 1H-, 19F- and 31P-nmr spectroscopy. Analytical, ir and mass spectral data are reported. The properties of these substances are compared with the corresponding thiophosphorylderivatives.

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