Journal of The American Society for Mass Spectrometry

, Volume 30, Issue 9, pp 1720–1732 | Cite as

Automatic Analyte-Ion Recognition and Background Removal for Ambient Mass-Spectrometric Data Based on Cross-Correlation

  • Yi You
  • Sunil P. Badal
  • Jacob T. ShelleyEmail author
Research Article


Ambient mass spectrometry is a powerful approach for rapid, high-throughput, and direct sample analysis. Due to the open-air desorption and ionization processes, random fluctuations of ambient conditions can lead to large variances in mass-spectral signals over time. The mass-spectral data also can be further complicated due to multiple analytes present in the sample, background-ion signals stemming from the desorption/ionization source itself, and other laboratory-specific conditions (e.g., ambient laboratory air, nearby hardware). Thus, background removal and analyte-ion recognition can be quite difficult, particularly in non-targeted analyses. Here, we demonstrate the use of a cross-correlation-based approach to exploit chemical information encoded in the time domain to group/categorize mass-spectral peaks from a single analysis dataset. Ions that originate from ambient (or other) background species were readily flagged and removed from spectra; the result was a decrease in mass-spectral complexity by over 70% due to the removal of these background ions. Meanwhile, analyte ions were differentiated and categorized based on their time-domain profiles. With sufficient mass resolving-power and mass-spectral acquisition rate, isolated mass spectra containing ions from the same species in a sample could be extracted, leading to a reduction in mass-spectral complexity by more than 98% in some cases. The cross-correlation approach was tested with different ionization sources as well as reproducible and irreproducible sample introduction. Software built in-house enabled fully automated data processing, which can be performed within a few seconds. Ultimately, this approach provides an additional dimension of analyte separation in ambient mass-spectrometric analyses with information that is already recorded throughout the analysis.


Ambient desorption/ionization Data processing DART FAPA MS/MS Cross-correlation 



We sincerely appreciate Dr. Leah Shriver and coworkers at the University of Akron for the kind donation of the γ-aminobutyric acid (GABA) standard used in this work.

Supplementary material

13361_2019_2246_MOESM1_ESM.docx (907 kb)
ESM 1 (DOCX 906 kb)


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Copyright information

© American Society for Mass Spectrometry 2019

Authors and Affiliations

  1. 1.Department of Chemistry and BiochemistryKent State UniversityKentUSA
  2. 2.Department of Chemistry and Chemical BiologyRensselaer Polytechnic InstituteTroyUSA

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