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Article

Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time–Frequency Analysis and Probabilistic Sparse Matrix Factorization

1
Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho, Nagoya 464-8601, Chikusa-ku, Japan
2
RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Yokohama 230-0045, Tsurumi-ku, Japan
3
Department of Information Systems, Niigata University of International and Information Studies, 3-1-1 Mizukino, Niigata 950-2292, Nishi-ku, Japan
4
Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Yokohama 230-0045, Tsurumi-ku, Japan
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(8), 2978; https://doi.org/10.3390/ijms21082978
Received: 27 March 2020 / Revised: 15 April 2020 / Accepted: 19 April 2020 / Published: 23 April 2020
Nuclear magnetic resonance (NMR) spectroscopy is commonly used to characterize molecular complexity because it produces informative atomic-resolution data on the chemical structure and molecular mobility of samples non-invasively by means of various acquisition parameters and pulse programs. However, analyzing the accumulated NMR data of mixtures is challenging due to noise and signal overlap. Therefore, data-cleansing steps, such as quality checking, noise reduction, and signal deconvolution, are important processes before spectrum analysis. Here, we have developed an NMR measurement informatics tool for data cleansing that combines short-time Fourier transform (STFT; a time–frequency analytical method) and probabilistic sparse matrix factorization (PSMF) for signal deconvolution and noise factor analysis. Our tool can be applied to the original free induction decay (FID) signals of a one-dimensional NMR spectrum. We show that the signal deconvolution method reduces the noise of FID signals, increasing the signal-to-noise ratio (SNR) about tenfold, and its application to diffusion-edited spectra allows signals of macromolecules and unsuppressed small molecules to be separated by the length of the T2* relaxation time. Noise factor analysis of NMR datasets identified correlations between SNR and acquisition parameters, identifying major experimental factors that can lower SNR. View Full-Text
Keywords: NMR; molecular complexity; FID; short-time Fourier transform; matrix factorization; T2* relaxation time; diffusion-edited spectrum; signal-to-noise ratio; acquisition parameters; correlation network analysis NMR; molecular complexity; FID; short-time Fourier transform; matrix factorization; T2* relaxation time; diffusion-edited spectrum; signal-to-noise ratio; acquisition parameters; correlation network analysis
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MDPI and ACS Style

Yamada, S.; Kurotani, A.; Chikayama, E.; Kikuchi, J. Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time–Frequency Analysis and Probabilistic Sparse Matrix Factorization. Int. J. Mol. Sci. 2020, 21, 2978. https://doi.org/10.3390/ijms21082978

AMA Style

Yamada S, Kurotani A, Chikayama E, Kikuchi J. Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time–Frequency Analysis and Probabilistic Sparse Matrix Factorization. International Journal of Molecular Sciences. 2020; 21(8):2978. https://doi.org/10.3390/ijms21082978

Chicago/Turabian Style

Yamada, Shunji, Atsushi Kurotani, Eisuke Chikayama, and Jun Kikuchi. 2020. "Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time–Frequency Analysis and Probabilistic Sparse Matrix Factorization" International Journal of Molecular Sciences 21, no. 8: 2978. https://doi.org/10.3390/ijms21082978

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