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.
ABSTRACT
RATIONALE: Gas chromatography/mass spectrometry (GC/MS) is a powerful analytical tool used to separate and then identify volatile compounds through library database searches. However, as not all compounds are registered in these databases, it is not uncommon to detect unregistered components. Therefore, new analytical techniques were developed that utilize methods of identification beyond database searches alone. METHODS: Acquire data by using electron ionization (EI) and soft ionization (SI) with high-resolution mass spectrometry (HRMS). Use the EI mass spectra to library search for matches. Use the SI mass spectra for accurate mass analysis of the EI molecular ions. Conduct an isotope pattern analysis of the molecular ion to refine the possible candidate compositions. Use these compositions as a constraint for the accurate mass analysis of the EI fragment ions. If a given molecular ion formula is not correct, the EI fragment ions will not show good matches. Finally, all analytical results are integrated into a color-coded qualitative analysis report. RESULTS: The capabilities of this new integrated analytical method were assessed for a polymer resin sample that was measured by using pyrolysis-gas chromatography/high resolution time-of-flight mass spectrometry. A total of 161 compounds were detected in the total ion current chromatogram, and 154 of these compounds were identified as having only one chemical formula candidate with this new integrated qualitative analysis method. CONCLUSIONS: This new integrated qualitative analysis method gives analytical results independent of library search results. It can be applied to a variety of SI methods including chemical ionization, photoionization, field ionization, and low-energy EI.
