Comprehensive Peak Characterization (CPC) in Untargeted LC–MS Analysis
Journal article, 2022

LC–MS-based untargeted metabolomics is heavily dependent on algorithms for automated peak detection and data preprocessing due to the complexity and size of the raw data generated. These algorithms are generally designed to be as inclusive as possible in order to minimize the number of missed peaks. This is known to result in an abundance of false positive peaks that further complicate downstream data processing and analysis. As a consequence, considerable effort is spent identifying features of interest that might represent peak detection artifacts. Here, we present the CPC algorithm, which allows automated characterization of detected peaks with subsequent filtering of low quality peaks using quality criteria familiar to analytical chemists. We provide a thorough description of the methods in addition to applying the algorithms to authentic metabolomics data. In the example presented, the algorithm removed about 35% of the peaks detected by XCMS, a majority of which exhibited a low signal-to-noise ratio. The algorithm is made available as an R-package and can be fully integrated into a standard XCMS workflow.

Peak characterization

Data processing

False peaks




Peak detection

Data quality

Peak filtering



Kristian Pirttilä

Uppsala University

David Balgoma

Uppsala University

Johannes Rainer

EURAC Research, Institute for Biomedicine

Curt Pettersson

Uppsala University

Mikael Hedeland

Uppsala University

Carl Brunius

Chalmers, Biology and Biological Engineering, Food and Nutrition Science


2218-1989 (ISSN)

Vol. 12 2 137

Subject Categories

Bioinformatics and Systems Biology

Signal Processing

Computer Vision and Robotics (Autonomous Systems)





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