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 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.