ABSTRACT
The formation of molecular and fragment ions observed in the field ionization mass spectrum of methyl stearate has been analyzed on the basis of quantum chemical calculations including time-dependent density functional theory (TDDFT) and natural bond orbital (NBO) analysis. The TDDFT calculations suggest that methyl stearate is ionized via two processes, namely a 7.43 eV excitation and a tunneling effect, while the high electric field of 1010 V/m enables analyte molecules to ionize at an effective 6 eV lower than the 9.26 eV ionization energy. The NBO analysis suggests that the abundances of aliphatic fragment ions [CnH2n+1]+ at m/z 29, 43, and 57 generated in the ionizing cell can be rationalized by hyperconjugation between the sigma (σ)-electrons of sp3 C-H bonds of methyl or methylene groups and the empty p-orbital of the carbocation -CH2+. The C4 periodic methyl ester fragment ions at m/z 115-269 and the complementary McLafferty rearrangement fragment ion at m/z 224 can be explained by metastable ion decay with rearrangement reactions in the ion source.
ABSTRACT
Electron ionization (EI) mass spectrum library searching is usually performed to identify a compound in gas chromatography/mass spectrometry. However, compounds whose EI mass spectra are registered in the library are still limited compared to the popular compound databases. This means that there are compounds that cannot be identified by conventional library searching but also may result in false positives. In this report, we report on the development of a machine learning model, which was trained using chemical formulae and EI mass spectra, that can predict the EI mass spectrum from the chemical structure. It allowed us to create a predicted EI mass spectrum database with predicted EI mass spectra for 100 million compounds in PubChem. We also propose a method for improving library searching time and accuracy that includes an extensive mass spectrum library.
