qBinning: Data Quality-Based Algorithm for Automized Ion Chromatogram Extraction from High-Resolution Mass Spectrometry

Due to the complexity and volume of data generated through
non-target
screening (NTS) using chromatographic couplings with high-resolution
mass spectrometry, automized processing routines are necessary. The
processing routines usually consist of many individual steps that
are user-parameter-dependent and, thus, require labor-intensive optimization.
Additionally, the effect of variations in raw data quality on the
processing results is unclear and not fully understood. Within this
work, we present qBinning, a novel algorithm for constructing extracted
ion chromatograms (EICs) based on statistical principles and, thus,
without the need to set user parameters. Furthermore, we give the
user feedback on the specific qualities of the generated EICs using
a scoring system (DQSbin). The DQSbin measures
reliability as it correlates with the probability of correct classification
of masses into EICs and the degree of overlap between different EIC
construction algorithms. This work is a big step forward in understanding
the behavior of NTS data and increasing the overall transparency in
the results of NTS.