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Area of Science:

  • Analytical Chemistry
  • Computational Chemistry

Background:

  • Gas chromatography-mass spectrometry (GC-MS) typically requires pure calibration standards for accurate concentration determination.
  • Existing methods for predicting ionization cross sections, such as the Fitch and Sauter correlation, have limitations in accuracy.

Purpose of the Study:

  • To develop a novel, highly accurate model for predicting relative total ionization cross sections for organic compounds in electron ionization mass spectrometry (EI-MS).
  • To enable concentration determination in GC-MS without the need for pure calibration standards.

Main Methods:

  • An atom- and group-based artificial neural network (FF-NN-AG) model was developed using 16 atom-type and 79 structural-group descriptors.
  • The model was trained on a database of 396 compounds, including 92 new experimental measurements, with a rigorous random sampling and testing protocol.
  • Performance was evaluated against the Fitch and Sauter correlation using metrics like r², root mean square deviation, and maximum relative error.

Main Results:

  • The FF-NN-AG model achieved a high accuracy with an r² of 0.992, significantly outperforming the Fitch and Sauter correlation (r² = 0.904).
  • The model demonstrated a substantially lower root mean square deviation (2.8 vs. 9.2) and maximum relative error (0.30 vs. 0.73).
  • A cross-section list was generated for sugars and anhydrosugars as a practical demonstration of the model's utility.

Conclusions:

  • The FF-NN-AG model provides a more accurate and reliable method for predicting EI cross sections compared to existing techniques.
  • This approach facilitates quantitative analysis in GC-MS by reducing or eliminating the requirement for pure calibration standards.
  • The model's ease of implementation and high accuracy make it a valuable tool for chemical analysis.