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

Reliable comparison of collision cross section (CCS) measurements across ion mobility platforms remains a key challenge for standardized molecular identification. We present a hierarchical, data-driven postcalibration correction framework that harmonizes CCS values across drift tube (DTIMS), trapped ion mobility (TIMS), and traveling wave (TWIMS) instruments, enabling quantitative cross-technology comparison despite differing calibration methods. Using a multilaboratory dataset of 840 measurements for 347 compounds, the framework reduced intertechnology CCS variability by approximately 95%, bringing residual differences within instrumental precision limits. Leave-one-compound-out validation demonstrated robust cross-technology transfer, lowering the median absolute percentage error from 8.9% to 3.2% and achieving 94.6% empirical coverage of 95% prediction intervals. The approach requires as few as three reference compounds, reducing recalibration workload by more than half, and provides full uncertainty estimates for probability-based database matching. This statistically grounded yet practical strategy enables harmonized, uncertainty-aware CCS databases and supports reproducible compound identification across laboratories and ion mobility technologies.