Predicting Total Electron-Ionization Cross Sections and GC-MS Calibration Factors Using Machine Learning

Concentrations in GC-MS using electron-ionization mass spectrometry can be determined without pure calibration standards through prediction of relative total-ionization cross sections. An atom- and group-based artificial neural network (FF-NN-AG) model is created to generate EI cross sections and calibrations for organic compounds. This model is easy to implement and is more accurate than the widely used atom-additivity-based correlation of Fitch and Sauter (Anal. Chem. 1983). Ninety-two new measurements of experimental EI cross sections (70-75 eV) are joined with different interlaboratory datasets, creating a 396-compound cross-section database, the largest to date. The FF-NN-AG model uses 16 atom-type descriptors, 79 structural-group descriptors, and one hidden layer of 10 nodes, trained 500 times. In each cycle, 96% of the compounds in this database are freshly chosen at random, and then the model is tested with the remaining 4%. The resulting r² is 0.992 versus 0.904 for the Fitch and Sauter correlation, root mean square deviation is 2.8 versus 9.2, and maximum relative error is 0.30 versus 0.73. As an example of the model's use, a list of cross sections is generated for various sugars and anhydrosugars.