Challenges in the decomposition of 2D NMR spectra of mixtures of small molecules

Cherni, Afef; Piersanti, Elena; Anthoine, Sandrine; Chaux, Caroline; Shintu, Laetitia; Yemloul, Mehdi; Torrésani, Bruno

doi:10.1039/c9fd00014c

Cited by 7 publications

(16 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The manual spectral deconvolution is dependent on user experience, which is a severe bottleneck in the field [17] , [23] . Additionally, it is an impractical and tedious process, especially for high-throughput applications and complex biological mixtures [24] , [25] . Semi-automated approaches have been developed to decompose TOCSY spectra into individual mixture components matching in NMR databases for identification [20] .…”

Section: Analysis Concept and Related Workmentioning

confidence: 99%

“…DeCoDeC identifies peaks apparent in specific pairs of TOCSY 1D cross-sections so that overlapping peaks associated with other metabolites are eliminated [20] . Significant limitations of both approaches are the peak shifts due to matrix effects which is the common case in metabolic profiling investigation of real-time evolution measurements [24] .…”

Section: Analysis Concept and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR)

Migdadi

Lambert

Telfah

et al. 2021

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

show abstract

Section: Analysis Concept and Related Workmentioning

confidence: 99%

Section: Analysis Concept and Related Workmentioning

confidence: 99%

Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR)

Migdadi

Lambert

Telfah

et al. 2021

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

show abstract

“…Many algorithms that adopt an alternating minimization procedure were proposed so as to solve the BSS problem such as ICA, SOBI, NMF, etc. A state-of-the-art of these algorithms used in the NMR context can be found in [15]. Based on this alternating procedure, we propose to solve Eq.…”

Section: Alternating Algorithm For Bssmentioning

confidence: 99%

“…The performances of the proposed approach are compared to the case when the popular data fidelity term ( 1 2 X − AS 2 F ) is used. In this case, we implemented the Block-Coordinate Variable Metric Forward Backward (BC-VMFB) algorithm [18] as it was used in [15]. Moreover, both of these algorithms are initialized with a projection of the JADE [19] result onto the nonnegative space, and run for a maximum of 15000 iterations.…”

Section: Processingmentioning

confidence: 99%

β-NMF and Sparsity Promoting Regularizations for Complex Mixture Unmixing - Application to 2D HSQC NMR

Cherni

Anthoine

Chaux

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

In Nuclear Magnetic Resonance (NMR) spectroscopy, an efficient analysis and a relevant extraction of different molecule properties from a given chemical mixture are important tasks, especially when processing bidimensional NMR data. To that end, using a blind source separation approach based on a variational formulation seems to be a good strategy. However, the poor resolution of NMR spectra and their large dimension require a new and modern blind source separation method. In this work, we propose a new variational formulation for blind source separation (BSS) based on a β-divergence data fidelity term combined with sparsity promoting regularization functions. An application to 2D HSQC NMR experiments illustrates the interest and the effectiveness of the proposed method whether in simulated or real cases.

show abstract

“…These studies are somewhat unique as they have no apparent intellectual conflicts of interest motivating results that indicate a particular technique or algorithm as being advantageous over other techniques. Cherni et al benchmarked projected alternate least squares, soft threshold projected alternate least squares, proximal alternating linearized minimization, block-coordinate variable metric forward-backward, and wavelet-based variants on these techniques using 1 H NMR datasets collected on mixtures of stock solutions and synthetic datasets [20]. Results were compared using an Amari index, signal to distortion ratio, signal to interference ratio, analyzed pure components, and knowledge of species quantities [20].…”

Section: Previous Applications Of Blind Source Separation To Nmr Datamentioning

confidence: 99%

Blind Source Separation for NMR Spectra with Negative Intensity

McCarty¹,

Ronghe²,

Woo³

et al. 2020

Preprint

View full text Add to dashboard Cite

NMR spectral datasets, especially in systems with limited samples, can be difficult to interpret if they contain multiple chemical components (phases, polymorphs, molecules, crystals, glasses, etc…) and the possibility of overlapping resonances. In this paper, we benchmark several blind source separation techniques for analysis of NMR spectral datasets containing negative intensity. For benchmarking purposes, we generated a large synthetic datasbase of quadrupolar solid-state NMR-like spectra that model spin-lattice T1 relaxation or nutation tip/flip angle experiments. Our benchmarking approach focused exclusively on the ability of blind source separation techniques to reproduce the spectra of the underlying pure components. In general, we find that FastICA (Fast Independent Component Analysis), SIMPLISMA (SIMPLe-to-use-Interactive Self-modeling Mixture Analysis), and NNMF (Non-Negative Matrix Factorization) are top-performing techniques. We demonstrate that dataset normalization approaches prior to blind source separation do not considerably improve outcomes. Within the range of noise levels studied, we did not find drastic changes to the ranking of techniques. The accuracy of FastICA and SIMPLISMA degrades quickly if excess (unreal) pure components are predicted. Our results indicate poor performance of SVD (Singular Value Decomposition) methods, and we propose alternative techniques for matrix initialization. The benchmarked techniques are also applied to real solid state NMR datasets. In general, the recommendations from the synthetic datasets agree with the recommendations and results from the real data analysis. The discussion provides some additional recommendations for spectroscopists applying blind source separation to NMR datasets, and for future benchmark studies. Applications of blind source separation to NMR datasets containing negative intensity may be especially useful for understanding complex and disordered systems with limited samples and mixtures of chemical components.

show abstract

Challenges in the decomposition of 2D NMR spectra of mixtures of small molecules

Abstract: In this paper we present a general approach for the blind identification of compounds from solutions using NMR spectroscopy and blind source separation algorithms.

Cited by 7 publications

References 30 publications

Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR)

Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR)

β-NMF and Sparsity Promoting Regularizations for Complex Mixture Unmixing - Application to 2D HSQC NMR

Blind Source Separation for NMR Spectra with Negative Intensity

Contact Info

Product

Resources

About