Improved Prediction of Carbonless NMR Spectra by the Machine Learning of Theoretical and Fragment Descriptors for Environmental Mixture Analysis-Reference-Cited by-同舟云学术

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Improved Prediction of Carbonless NMR Spectra by the Machine Learning of Theoretical and Fragment Descriptors for Environmental Mixture Analysis

Published:2021-04-30 Issue:18 Volume:93 Page:6901-6906
ISSN:0003-2700
Container-title:Analytical Chemistry
language:en
Short-container-title:Anal. Chem.

Author:

Ito Kengo¹²,Xu Xiangru³,Kikuchi Jun¹²⁴^ORCID

Affiliation:

1. RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

2. Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

3. Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland

4. Graduate School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan

Funder

Agriculture, Forestry and Fisheries Technology Council

Publisher

American Chemical Society (ACS)

Subject

Analytical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.1c00756

Reference39 articles.

1. Investigation of exchange processes by two‐dimensional NMR spectroscopy

2. A 2D proton J-resolved NMR method for direct measurements on heterogeneous foods

3. Freshness assessment of intact fish via 2D 1H J-resolved NMR spectroscopy combined with pattern recognition methods

4. Application of Two-Dimensional Nuclear Magnetic Resonance for Signal Enhancement by Spectral Integration Using a Large Data Set of Metabolic Mixtures

5. Tackling the Peak Overlap Issue in NMR Metabolomics Studies: 1D Projected Correlation Traces from Statistical Correlation Analysis on Nontilted 2D 1H NMR J-Resolved Spectra

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