Determining Collision Cross Sections from Differential Ion Mobility Spectrometry

Abstract

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.

The hitchhiker's guide to dynamic ion–solvent clustering: applications in differential ion mobility spectrometry

“…Using the same dataset, the random-forest algorithm was retrained for CCS predictions using the ion's m/z and SV/CV pairs between SV = 1500 and 4000 V as input features. 134 Fig. 9B shows a correlation plot of CCS as determined by our ML model and as calculated using MobCal-MPI.…”

“…Having identified correlations between DMS-measured clustering behaviour and ion-solvent binding energies, we explored the possibility of correlation with other molecular physicochemical properties. 52,134,135 Indeed, when investigating a series of 2-methylquinolin-8-ol derivatives, strong correlations were identified between DMS behaviour and condensed-phase properties such as pK a , pK b , and passive cell permeability. 52 Fig.…”

“…140 (B) The correlation between MobCal-calculated and MLpredicted CCSs for more than 400 analytes from 11 chemical classes. 134 Training set (black points) and validation set data (red points) indicate that a mean percent error of 2.6% is expected for unknown analytes (light-blue shading). model errors will continue to decrease as the size of the data set increases.…”

Recent developments in data acquisition, treatment and analysis with ion mobility‐mass spectrometry for lipidomics

“…Although this greatly overcomes the problem of the accuracy in the CCS values obtained in TWIMS, entangles a problem of correctly choosing the calibrant in TWIMS. A recent approach allows the CCS calculation in DMS using a machine learning-based calibration [78].…”

The combination of laser photodissociation, action spectroscopy, and mass spectrometry to identify and separate isomers

“…Therefore, even without PD spectroscopy, the CCS values can provide information on structural assignments of different isomers separated using IMS. A method to measure CCS values using FAIMS results is emerging, 195 which is promising but not as established as CCS measurements based on drift IMS.…”