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

Yi-Hui Zhou presents a hierarchical Bayesian framework for harmonizing collision cross section (CCS) measurements across ion mobility platforms. The dataset includes 840 measurements for 347 compounds from a multilaboratory study, used to validate the framework. The approach reduced intertechnology CCS variability by approximately 95% and lowered median absolute percentage error from 8.9% to 3.2%.

Use Cases

• Harmonizing CCS measurements across drift tube, trapped ion mobility, and traveling wave instruments based on the described framework.
• Building probability-based database matching systems based on the full uncertainty estimates provided by the method.
• Reducing recalibration workload for cross-technology comparison based on the requirement of as few as three reference compounds.
• Validating cross-technology transfer of CCS values based on the described leave-one-compound-out validation results.

Strengths

• Framework validation uses a dataset of 840 measurements for 347 compounds.
• The method reduced intertechnology CCS variability by approximately 95%.
• Leave-one-compound-out validation lowered median absolute percentage error from 8.9% to 3.2% and achieved 94.6% empirical coverage of 95% prediction intervals.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 15.6 KB, indicating limited scope.

Provenance

Source

Yi-Hui Zhou via figshare.

Collection Method

Multilaboratory dataset of ion mobility spectrometry measurements.

Time Range

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Freshness

Last updated 2026-05-08 18:33:23; freshness should be verified.

Geography

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