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Related Experiment Video

Updated: May 10, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

A Unified Bayesian Framework for Cross-Technology Collision Cross Section Postcalibration Correction and

Yi-Hui Zhou¹, George Sun²

¹Departments of Biological Sciences and Statistics, North Carolina State University, Raleigh, North Carolina 27695, United States.

Analytical Chemistry

|May 8, 2026

Summary

A new framework harmonizes collision cross section (CCS) measurements across different ion mobility platforms, significantly reducing data variability for standardized molecular identification. This approach enhances cross-technology comparison and supports reproducible compound identification.

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Operation of the Collaborative Composite Manufacturing (CCM) System

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Published on: October 1, 2019

Area of Science:

Analytical Chemistry
Physical Chemistry

Background:

Standardized molecular identification relies on comparable collision cross section (CCS) measurements, which are challenging across diverse ion mobility platforms.
Existing calibration methods differ significantly between drift tube (DTIMS), trapped ion mobility (TIMS), and traveling wave (TWIMS) instruments, hindering data interoperability.

Purpose of the Study:

To develop and validate a data-driven postcalibration correction framework for harmonizing CCS values across DTIMS, TIMS, and TWIMS.
To enable quantitative, cross-technology comparisons of CCS measurements for improved molecular identification.

Main Methods:

A hierarchical, data-driven framework was developed for postcalibration correction of CCS data.
The framework was validated using a multilaboratory dataset of 840 measurements for 347 compounds.
Leave-one-compound-out cross-validation was employed to assess transferability and accuracy.

Main Results:

The framework reduced intertechnology CCS variability by approximately 95%.
Median absolute percentage error decreased from 8.9% to 3.2%, with 94.6% empirical coverage of 95% prediction intervals.
The approach requires minimal reference compounds (as few as three) and halves recalibration workload.

Conclusions:

The developed framework effectively harmonizes CCS measurements across different ion mobility platforms.
This strategy enables statistically grounded, uncertainty-aware CCS databases for reproducible compound identification.
The approach supports reliable cross-laboratory and cross-technology molecular identification.