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This study introduces an automated mass spectral calibration method using mass differences, achieving high accuracy for complex sample analysis. The open-source PyC2MC software platform facilitates molecular formula assignments and batch processing.

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Computational Chemistry

Background:

  • Traditional mass spectral calibration relies on internal calibrants, which can be problematic for complex samples.
  • Accurate mass calibration is crucial for confident elemental and molecular formula assignments.

Purpose of the Study:

  • To develop and validate a fully automated mass spectral calibration method using mass differences.
  • To improve the accuracy and efficiency of molecular formula assignment for complex mixtures.

Main Methods:

  • Introduction of the mass difference spectrum for elemental formula assignment.
  • Optimization of measured mass differences for recalibration to sub-ppm error without known masses.
  • Development of a "walking" calibration using matrix intersection for molecular formulas.
  • Implementation in the open-source Python platform, PyC2MC.

Main Results:

  • Confident elemental formulas assigned from abundant mass differences (0-50 Da), even with poor initial calibration.
  • Recalibration achieved to as low as 80 ppb root-mean-square error.
  • Successful application to diverse complex samples including petroleum, organic matter, polymers, and batteries.
  • Identification of characteristic mass differences arising from various chemical processes.

Conclusions:

  • The novel mass difference-based calibration method offers a robust and automated approach for complex mass spectral data.
  • PyC2MC enables efficient processing of time-resolved data and accurate molecular formula determination.
  • This method enhances the analysis of complex chemical systems and processes.