ArticleInternational journal of molecular sciences2020
Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time-Frequency Analysis and Probabilistic Sparse Matrix Factorization.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Differentiation of closely-related species within Acinetobacter baumannii-calcoaceticus complex via Raman spectroscopy: a comparative machine learning analysis.World journal of microbiology & biotechnology · 2024Article
- Materials informatics approach using domain modelling for exploring structure-property relationships of polymers.Scientific reports · 2022Article
- The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science.RSC advances · 2021Review
- AnMetabolites · 2021Article
- Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN).ACS omega · 2021Article
- Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials.International journal of molecular sciences · 2021Article
- Special Issue "Selected Papers from the 8th Asia-Pacific NMR (APNMR) Symposium: Recent Advances in NMR Spectroscopy".International journal of molecular sciences · 2020Article
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Authors and funding
4 authors.
Funding
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Abstract
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
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