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Updated: Apr 30, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Method validation strategies involved in non-targeted metabolomics.

Shama Naz¹, Maria Vallejo¹, Antonia García¹

¹CEMBIO (Center for Metabolomics and Bioanalysis), Facultad de Farmacia, Universidad San Pablo CEU, Madrid, Spain.

Journal of Chromatography. A

|May 10, 2014

Summary

Non-targeted metabolomics provides a global view of metabolites for biomedical research. This review discusses validation strategies for developing reliable analytical methods in this field.

Keywords:

Analytical method validation Metabolomics Non-targeted approach

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Last Updated: Apr 30, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

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Published on: May 27, 2014

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

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Published on: May 20, 2013

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

Area of Science:

Metabolomics
Analytical Chemistry
Biomedical Research

Background:

Non-targeted metabolomics analyzes all small molecules in a biological system.
It aids in disease diagnosis and understanding pathological mechanisms.
Methods often involve NMR and MS with separation techniques.

Purpose of the Study:

To review current validation strategies for non-targeted metabolomics.
To recommend steps for developing reliable analytical methods.
To address the unique challenges in validating unbiased global analyses.

Main Methods:

Data acquisition using NMR and MS.
Chemometric analysis for data interpretation.
Database searching for metabolite identification.

Main Results:

Validation is crucial for reliable and reproducible results in non-targeted metabolomics.
Analytical challenges differ significantly from targeted methods.
Existing validation strategies are described and evaluated.

Conclusions:

Robust validation is essential for the successful application of non-targeted metabolomics.
Method development requires careful consideration of specific analytical challenges.
Standardized validation approaches will enhance the utility of metabolomics in biomedical discovery.