Benefits of Ion Mobility Separation in GC-APCI-HRMS Screening: From the Construction of a CCS Library to the Application to Real-World Samples

Abstract

The performance of gas chromatography (GC) combined with the improved identification properties of ion mobility separation coupled to high-resolution mass spectrometry (IMS-HRMS) is presented as a promising approach for the monitoring of (semi)volatile compounds in complex matrices. The soft ionization promoted by an atmospheric pressure chemical ionization (APCI) source designed for GC preserves the molecular and/or quasi-molecular ion information enabling a rapid, sensitive, and efficient wide-scope screening. Additionally, ion mobility separation (IMS) separates species of interest from coeluting matrix interferences and/or resolves isomers based on their charge, shape, and size, making IMS-derived collision cross section (CCS) a robust and matrix-independent parameter comparable between instruments. In this way, GC-APCI-IMS-HRMS becomes a powerful approach for both target and suspect screening due to the improvements in (tentative) identifications. In this work, mobility data for 264 relevant multiclass organic pollutants in environmental and food-safety fields were collected by coupling GC-APCI with IMS-HRMS, generating CCS information for molecular ion and/or protonated molecules and some in-source fragments. The identification power of GC-APCI-IMS-HRMS for the studied compounds was assessed in complex-matrix samples, including fish feed extracts, surface waters, and different fruit and vegetable samples.

Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants

“…48 for [M] + * were finally reported. 58 The undisclosed CCS data could limit the chemical space of this CCS compendium. The correlation between CCS values and mass for 9407 ions and the distribution of CCS and mass values are shown together in Figure 1A.…”

“…36 The high number of CCS values measured by TWIMS since 2016 can be attributed to the introduction of the Vion IMSquadrupole time-of-flight mass spectrometry (IMS-QTOF) by Waters Corporation in 2015. In this CCS compendium, 3353 CCS values from nine publications were determined using a Vion IMS-QTOF, 21,33,35,46,47,49,55,58,60 and the CCS databases for steroids and mycotoxins were developed using both the Vion and Synapt platforms. 30,32 A comparison of the empirical CCS values measured via DTIMS and TWIMS devices was performed, and the CCS deviations were calculated using the corresponding DT 49,52,55 were found for the [M + H] + adduct of bisoprolol; thus, six CCS deviations were finally calculated for this ion.…”

“…A comparison of CCS values acquired from GC–APCI–TWIMS–HRMS and LC–ESI–TWIMS–HRMS was conducted by Izquierdo-Sandoval and co-workers . A high level of consistency between the CCS values measured by both platforms was observed, with 83.3% (70 out of 84) of the molecules showing CCS deviations of less than 1% and 98.8% (83 out of 84) having CCS deviations under 2%.…”

Sliding Windows in Ion Mobility (SWIM): A New Approach to Increase the Resolving Power in Trapped Ion Mobility-Mass Spectrometry Hyphenated with Chromatography

“…First, it adds an additional "orthogonal" dimension of separation to mass-to-charge ratios and retention times, enabling the removal of some coeluting interferences and leading to cleaner MS and tandem MS spectra. 15 Second, the experimentally measured ion mobility can usually be converted to a collision cross-section (CCS) value, which is an intrinsic parameter of the analyte that is dependent on the experimental conditions. The CCS value can be compared to experimentally derived or in silico calculated values available in libraries and serve as an additional analyte identification point along with accurate mass, ion ratios, retention time, and tandem mass spectrum in both targeted 16,17 and untargeted approaches.…”

Collision Cross Section Prediction with Molecular Fingerprint Using Machine Learning

“…Drift tube ion mobility (DTIM) and traveling wave ion mobility (TWIM) are two of the most used IMS techniques to measure the CCS value or drift time [ 37 , 39 ]. CCS value and drift time have been employed in NTS as an additional source of information to increase confidence level in structural elucidation [ 40 , 41 , 42 ]. In addition to experimentally defined CCS values, CCS values can be estimated/predicted via theoretical calculations or Machine Learning (ML) [ 43 , 44 ].…”