Determining Collision Cross Sections from Differential Ion Mobility Spectrometry

Determining Collision Cross Sections from Differential Ion Mobility Spectrometry

The experimental determination of ion-neutral collision cross sections (CCSs) is generally confined to ion mobility spectrometry (IMS) technologies that operate under the so-called low-field limit or those that enable empirical calibration strategies (e.g., traveling wave IMS; TWIMS). Correlation of...

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Bibliographic Details
Published in:Analytical chemistry (Washington) Vol. 93; no. 25; pp. 8937 - 8944
Main Authors: Ieritano, Christian, Lee, Arthur, Crouse, Jeff, Bowman, Zack, Mashmoushi, Nour, Crossley, Paige M, Friebe, Benjamin P, Campbell, J. Larry, Hopkins, W. Scott
Format: Journal Article
Language:English
Published: United States American Chemical Society 29-06-2021
Subjects:
Calibration
Cations
Chemistry
Cross-sections
Electric fields
Empirical analysis
Ion trajectories
Ionic mobility
Ions
Learning algorithms
Machine learning
Mobility
Regression models
Scientific imaging
Spectrometry
Spectroscopy
Statistical analysis
Training
Traveling waves
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Summary:The experimental determination of ion-neutral collision cross sections (CCSs) is generally confined to ion mobility spectrometry (IMS) technologies that operate under the so-called low-field limit or those that enable empirical calibration strategies (e.g., traveling wave IMS; TWIMS). Correlation of ion trajectories to CCS in other non-linear IMS techniques that employ dynamic electric fields, such as differential mobility spectrometry (DMS), has remained a challenge since its inception. Here, we describe how an ion’s CCS can be measured from DMS experiments using a machine learning (ML)-based calibration. The differential mobility of 409 molecular cations (m/z: 86–683 Da and CCS 110–236 Å2) was measured in a N2 environment to train the ML framework. Several open-source ML routines were tested and trained using DMS-MS data in the form of the parent ion’s m/z and the compensation voltage required for elution at specific separation voltages between 1500 and 4000 V. The best performing ML model, random forest regression, predicted CCSs with a mean absolute percent error of 2.6 ± 0.4% for analytes excluded from the training set (i.e., out-of-the-bag external validation). This accuracy approaches the inherent statistical error of ∼2.2% for the MobCal-MPI CCS calculations employed for training purposes and the <2% threshold for matching literature CCSs with those obtained on a TWIMS platform.
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ISSN:0003-2700
1520-6882
DOI:10.1021/acs.analchem.1c01420