Data-Driven Filter for Detector Oscillation Artifacts in Time-of-Flight Mass Spectrometry

Time-of-flight mass spectrometry (TOF-MS) is widely used for complex mixture analysis due to its ability to detect thousands of chemical compounds in a single sample. However, high-intensity ions can saturate the detector and generate artifacts, commonly referred to as "detector ringing". These artifacts resemble true MS features and often persist after componentization, complicating data interpretation and feature prioritization. Here, we introduce a data-driven filtering approach that exploits the time-domain origin of detector ringing to remove such artifacts. Because ion flight times scale with $\sqrt{m/z}$, oscillations in the detector response manifest as artifactual features with constant delays in $\sqrt{m/z}$, providing a universal detection criterion independent of ion source conditions and chromatographic or ion mobility separation. The workflow was evaluated on GC-, LC-, and ion mobility-hyphenated TOF-MS datasets from multiple instrumental platforms, identifying and removing up to 5.3% of features. Comparison across instruments revealed that $\Delta \sqrt{m/z}$ spacings are not only constant but also instrument-specific and polarity-independent, suggesting potential applications in data forensics and traceability. To assess broader relevance, the filter was applied to the MassBank and NIST DART-MS Forensics fragmentation mass spectral libraries. Although the overall incidence was low relative to library size (1.7%), over 1000 mass spectra were provisionally identified as containing ringing artifacts. Clustering these spectra by their $\Delta \sqrt{m/z}$ effectively partitioned the data by TOF-MS platform, revealing distinct groupings across libraries and contributors. By specifically targeting detector-derived artifacts, this workflow reduces false positives and improves feature prioritization and compound identification, enhancing the robustness of TOF-MS-based chemical analyses.