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Updated: Oct 25, 2025

Characterizing Bacterial Volatiles using Secondary Electrospray Ionization Mass Spectrometry SESI-MS
Published on: June 8, 2011
Quantitative electrospray ionization efficiency scale: 10 years after
Merit Oss1, Sofja Tshepelevitsh1, Anneli Kruve1
1Institute of Chemistry, University of Tartu, Tartu, Estonia.
Rationale:
The first comprehensive quantitative scale of the efficiency of electrospray ionization (ESI) in the positive mode by monoprotonation, containing 62 compounds, was published in 2010. Several trends were found between the compound structure and ionization efficiency (IE) but, possibly because of the limited diversity of the compounds, some questions remained. This work undertakes to align the new data with the originally published IE scale and carry out statistical analysis of the resulting more extensive and diverse data set to derive more grounded relationships and offer a possibility of predicting logIE values.
Methods:
Recently, several new IE studies with numerous compounds have been conducted. In several of them, more detailed investigations of the influence of compound structure, solvent properties, or instrument settings have been conducted. IE data from these studies and results from this work were combined, and the multilinear regression method was applied to relate IE to various compound parameters.
Results:
The most comprehensive IE scale available, containing 334 compounds of highly diverse chemical nature and spanning 6 orders of magnitude of IE, has been compiled. Several useful trends were revealed.
Conclusions:
The ESI ionization efficiency of a compound by protonation is mainly affected by three factors: basicity (expressed by pKaH in water), molecular size (expressed by molar volume or surface area), and hydrophobicity of the ion (expressed by charge delocalization in the ion or its partition coefficient between a water-acetonitrile mixture and hexane). The presented models can be used for tentative prediction of logIE of new compounds (under the used conditions) from parameters that can be computed using commercially available software. The root mean square error of prediction is in the range of 0.7-0.8 log units.
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