Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

Lunzhao Yi, Naiping Dong, Yonghuan Yun, Baichuan Deng, Dabing Ren, Shao Liu, Yizeng Liang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

242 Citations (Scopus)

Abstract

This review focuses on recent and potential advances in chemometric methods in relation to data processing in metabolomics, especially for data generated from mass spectrometric techniques. Metabolomics is gradually being regarded a valuable and promising biotechnology rather than an ambitious advancement. Herein, we outline significant developments in metabolomics, especially in the combination with modern chemical analysis techniques, and dedicated statistical, and chemometric data analytical strategies. Advanced skills in the preprocessing of raw data, identification of metabolites, variable selection, and modeling are illustrated. We believe that insights from these developments will help narrow the gap between the original dataset and current biological knowledge. We also discuss the limitations and perspectives of extracting information from high-throughput datasets.

Original languageEnglish
Pages (from-to)17-34
Number of pages18
JournalAnalytica Chimica Acta
Volume914
DOIs
Publication statusPublished - 31 Mar 2016

Keywords

  • Biomarker
  • Chemometrics
  • Data preprocessing
  • Identification of metabolites
  • Metabolomics
  • Modeling

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Environmental Chemistry
  • Spectroscopy

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