Literature DB >> 26783568

SENSI: signal enhancement by spectral integration for the analysis of metabolic mixtures.

Takuma Misawa1, Takanori Komatsu1, Yasuhiro Date1, Jun Kikuchi2.   

Abstract

The method provided here can overcome the low S/N problem in (13)C NMR, by the integration of plural spectra to take advantage of high-resolution potential based on non-bucketing analysis without additional measurements. In addition, a new metabolite annotation approach using advanced STOCSY and quantum chemistry calculations was introduced in this study.

Mesh:

Year:  2016        PMID: 26783568     DOI: 10.1039/c5cc09442a

Source DB:  PubMed          Journal:  Chem Commun (Camb)        ISSN: 1359-7345            Impact factor:   6.222


  5 in total

Review 1.  Recent Advances in NMR-Based Metabolomics.

Authors:  G A Nagana Gowda; Daniel Raftery
Journal:  Anal Chem       Date:  2016-12-02       Impact factor: 6.986

2.  Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet.

Authors:  Yuka Shiokawa; Yasuhiro Date; Jun Kikuchi
Journal:  Sci Rep       Date:  2018-02-21       Impact factor: 4.379

3.  Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals.

Authors:  Kengo Ito; Yuka Obuchi; Eisuke Chikayama; Yasuhiro Date; Jun Kikuchi
Journal:  Chem Sci       Date:  2018-09-10       Impact factor: 9.825

4.  Systemic Homeostasis in Metabolome, Ionome, and Microbiome of Wild Yellowfin Goby in Estuarine Ecosystem.

Authors:  Feifei Wei; Kenji Sakata; Taiga Asakura; Yasuhiro Date; Jun Kikuchi
Journal:  Sci Rep       Date:  2018-02-22       Impact factor: 4.379

5.  NMR-TS: de novo molecule identification from NMR spectra.

Authors:  Jinzhe Zhang; Kei Terayama; Masato Sumita; Kazuki Yoshizoe; Kengo Ito; Jun Kikuchi; Koji Tsuda
Journal:  Sci Technol Adv Mater       Date:  2020-07-30       Impact factor: 8.090

  5 in total

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