Improved Discrimination of Mass Spectral Isomers Using the High-Dimensional Consensus Mass Spectral Similarity...

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Computational Chemistry

Background:

• Distinguishing isomers with similar properties is a significant challenge in mass spectrometry.
• Traditional methods like cosine similarity can struggle with closely related compounds.
• High-dimensional consensus mass spectral (HDCMS) similarity scoring offers a novel approach.

Purpose of the Study:

• To evaluate the efficacy of the HDCMS method for discriminating terpene isomers using electron ionization mass spectrometry (EI-MS).
• To assess the performance of HDCMS on isomers with similar retention indices and high cosine similarity scores.
• To investigate the impact of replicate measurements on isomer discrimination.

Main Methods:

• Application of HDCMS similarity scoring to electron ionization mass spectra of 7 terpene isomers.
• Utilizing gas chromatography-electron ionization-mass spectrometry (GC-EI-MS) with 15 replicate spectra per isomer.
• Formulating isomer discrimination within a mathematical Hilbert space framework.

Main Results:

• The HDCMS algorithm successfully discriminated all tested terpene isomers.
• The method demonstrated potential for distinguishing compounds with minimal differences in retention indices (<30) and high initial similarity scores (>0.9).
• A heuristic study on the required number of replicates for discrimination was presented.

Conclusions:

• HDCMS is a promising technique for isomer discrimination in EI-MS data.
• The method's reliance on replicate measurements provides a robust signature for distinguishing similar compounds.
• Further discussion on the benefits and limitations of HDCMS for EI-MS applications is provided.