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Generalized Kendrick analysis for improved visualization of atmospheric mass spectral data

Mitchell W. Alton, Harald J. Stark, Manjula R. Canagaratna, and Eleanor C. Browne

Atmospheric Measurement Techniques

Atmos. Meas. Tech., 16, 3273–3282, 2023

Publication Date: June 29, 2023

 

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Abstract. 

Mass spectrometry is an important analytical technique within the field of atmospheric chemistry. Owing to advances in instrumentation, particularly with regards to mass-resolving power and instrument response factors (sensitivities), hundreds of different mass-to-charge (m/z) signals are routinely measured. This large number of detected ions creates challenges for data visualization. Furthermore, assignment of chemical formulas to these ions is time consuming and increases in difficulty at the higher m/z ranges. Here, we describe generalized Kendrick analysis (GKA) to facilitate the visualization and peak identification processes for typical atmospheric organic (and to some extent inorganic) compounds. GKA is closely related to resolution-enhanced Kendrick mass defect analysis (REKMD), which introduces a tunable integer into the Kendrick equation that effectively contracts or expands the mass scale. A characteristic of all Kendrick analysis methods is that these changes maintain the horizontal alignment of ion series related by integer multiples of the chosen base unit. Compared to traditional Kendrick analysis, GKA and REKMD use a tunable parameter (“scaling factor”) to alter the mass defect spacing between different homologue ion series. As a result, the entire mass defect range (−0.5 to 0.5) is more effectively used simplifying data visualization and facilitating chemical formula assignment. We describe the mechanism of this transformation and discuss base unit and scaling factor selections appropriate for compounds typically found in atmospheric measurements. We present an open-source graphical user interface (GUI) for calculating and visualizing GKA results within the Igor Pro environment.