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Updated: Aug 23, 2025

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
Published on: March 14, 2013
A system suitability testing platform for untargeted, high-resolution mass spectrometry
Andrei Dmitrenko1,2, Michelle Reid1, Nicola Zamboni1
1Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland.
Untargeted metabolomics requires a new system suitability testing platform for time-of-flight mass spectrometers. This approach verifies instrument performance before analysis, ensuring data quality and enabling future harmonization.
Area of Science:
- Analytical Chemistry
- Metabolomics
- Mass Spectrometry
Background:
- Untargeted metabolomics faces challenges in harmonizing measurements over time due to the chemical complexity of samples.
- Internal isotopic standards are insufficient for normalizing untargeted metabolomics data post-analysis.
- Verifying instrument performance prior to sample injection is crucial for reliable data.
Purpose of the Study:
- To develop and evaluate a system suitability testing (SST) platform for time-of-flight mass spectrometers (TOF-MS) independent of liquid chromatography.
- To establish a method for a priori verification of instrument performance in untargeted metabolomics.
Main Methods:
- A chemically defined quality control mixture was used with a fast acquisition method.
- Software was developed to extract approximately 3,000 numerical features from profile data.
- A web service was implemented for monitoring instrument performance over a 21-month pilot study.
Main Results:
- The SST platform demonstrated effectiveness in detecting anomalies in instrument performance.
- Causal relationships between spectral features and machine settings were identified.
- Illustrative results showcase the potential for anomaly detection and performance monitoring.
Conclusions:
- The proposed SST platform provides a robust method for a priori instrument performance verification in untargeted metabolomics.
- The system enables anomaly detection, supports preventive maintenance, and offers features for future data harmonization.
- This approach addresses critical challenges in ensuring data quality and comparability in longitudinal metabolomics studies.
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