Retention Index Prediction Using Quantitative Structure–Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics-Reference-Cited by-同舟云学术

Retention Index Prediction Using Quantitative Structure–Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics

Published:2018-06-28 Issue:15 Volume:90 Page:9434-9440
ISSN:0003-2700
Container-title:Analytical Chemistry
language:en
Short-container-title:Anal. Chem.

Author:

Wen Yabin¹,Amos Ruth I. J.¹^ORCID,Talebi Mohammad¹,Szucs Roman²,Dolan John W.³,Pohl Christopher A.⁴^ORCID,Haddad Paul R.¹^ORCID

Affiliation:

1. Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, 7001 Tasmania, Australia

2. Pfizer Global Research and Development, Sandwich CT139NJ, U.K.

3. LC Resources, McMinnville, Oregon 97128, United States

4. Thermo Fisher Scientific, Sunnyvale, California 94085, United States

Funder

Australian Research Council

Publisher

American Chemical Society (ACS)

Subject

Analytical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.8b02084

Reference40 articles.

1. Retention Time Prediction Improves Identification in Nontargeted Lipidomics Approaches

2. Targeted Metabolomics for Biomarker Discovery

3. Method validation strategies involved in non-targeted metabolomics

4. Quantitative structure–retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: Endogenous metabolites and banned compounds

5. Metabolite identification: are you sure? And how do your peers gauge your confidence?

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