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

Area of Science:

• Analytical Chemistry
• Spectrometry
• Chemical Analysis

Background:

• Ambient mass spectrometry offers rapid, direct sample analysis but suffers from signal instability due to environmental fluctuations.
• Mass spectral data complexity arises from multiple analytes, background ions, and laboratory conditions, hindering non-targeted analyses.
• Current methods face challenges in background removal and accurate analyte-ion recognition.

Purpose of the Study:

• To develop and demonstrate a novel cross-correlation-based approach for analyzing time-domain chemical information in ambient mass spectrometry.
• To reduce mass-spectral complexity by effectively removing background ions and categorizing analyte signals.
• To enhance analyte separation and data interpretation in ambient mass spectrometry.

Main Methods:

• Utilized a cross-correlation approach to analyze the time-domain profiles of mass-spectral peaks within a single dataset.
• Developed in-house software for automated data processing, enabling rapid analysis.
• Tested the method with various ionization sources and sample introduction reproducibility.

Main Results:

• Successfully flagged and removed background ions, reducing spectral complexity by over 70%.
• Differentiated and categorized analyte ions based on their unique time-domain profiles.
• Achieved significant reductions in mass-spectral complexity (over 98% in some cases) by isolating analyte-specific spectra.

Conclusions:

• The cross-correlation approach provides an effective method for background removal and analyte categorization in ambient mass spectrometry.
• This technique adds a new dimension of analyte separation by leveraging time-domain information inherent in the data.
• Automated processing allows for rapid and efficient data analysis, improving the utility of ambient mass spectrometry.