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Atomic Emission Spectroscopy: Interference01:30

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In atomic emission spectroscopy (AES), high-temperature atomizers excite a broad range of elements and molecules that generate complex emissions from sources such as oxides, hydroxides, and flame combustion products in the flame or plasma. Several strategies can be employed to minimize spectral interferences caused by overlapping emission lines or bands. These include increasing instrument resolution, choosing alternative emission lines, optimally placing the detector in low-background regions,...
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In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
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AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
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Refining EI-MS library search results through atomic-level insights.

Islambek Ashyrmamatov1, Umit V Ucak2, Juyong Lee3,4,5,6

  • 1College of Pharmacy, Seoul National University, Seoul, Republic of Korea.

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This study introduces a novel method using atomic environments to interpret mass spectra, improving structure elucidation and peak annotation. The Transformer model enhances existing library search methods for more accurate compound identification.

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Area of Science:

  • Computational Chemistry
  • Spectroscopy
  • Cheminformatics

Background:

  • Mass spectrometry (MS) data complexity hinders accurate structure elucidation and peak annotation.
  • Existing methods struggle with direct correlation between spectral and structural similarities.

Purpose of the Study:

  • To refine and re-rank candidate structures from library searches using predicted atomic environments.
  • To improve the interpretation of electron ionization mass spectrometry (EI-MS) data.

Main Methods:

  • Utilized modified atomic environments (topological radii zero) to represent spectral peaks.
  • Developed a Transformer model trained on mass-to-fragment mappings.
  • Predicted atomic environments directly from mass and intensity data.

Main Results:

  • Achieved 86.1% precision and 78.4% recall for peak prediction on the test set.
  • Demonstrated improved spectra matching by suggesting atomic environment inclusion/exclusion.
  • Validated findings using the NIST database.

Conclusions:

  • The novel framework complements conventional methods for spectra matching.
  • Provides deeper insights into structural contents from EI-MS data.
  • Enhances compound identification through atomic-level analysis.