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Area of Science:

  • Analytical Chemistry
  • Computational Chemistry
  • Statistical Modeling

Background:

  • Traditional chromatographic data analysis often involves separate models for each analyte and solvent, limiting information sharing.
  • Developing predictive models for chromatographic retention factors is crucial for method development and optimization.
  • Existing methods may not fully leverage correlations between analytes or solvent effects.

Purpose of the Study:

  • To develop and evaluate a multilevel (hierarchical) regression model for isocratic reversed-phase high-performance liquid chromatography (RP-HPLC) data.
  • To compare the performance of multilevel models with varying degrees of pooling against traditional methods.
  • To demonstrate the utility of the developed model for predicting analyte retention factors across different mobile phases (methanol and acetonitrile).

Main Methods:

  • Utilized isocratic RP-HPLC data for 58 chemical compounds in methanol and acetonitrile mobile phases.
  • Developed and compared multilevel models with no pooling, partial pooling, and partial pooling with quantitative structure-retention relationships (QSRR).
  • Employed Bayesian inference with Markov-chain Monte Carlo (MCMC) sampling using Stan software for statistical analysis.
  • Validated models using 10-fold cross-validation.

Main Results:

  • Multilevel models demonstrated superior performance in inference and prediction compared to non-pooled models.
  • Partial pooling models effectively shared information across analytes and solvent systems.
  • The developed QSRR-integrated multilevel model provided accurate predictions of retention factors.
  • The models facilitated a better understanding of the chromatographic data.

Conclusions:

  • Multilevel modeling offers a powerful and statistically robust framework for analyzing chromatographic data.
  • This approach enhances the predictive accuracy of retention factors, particularly when transferring data between different organic modifiers.
  • The developed models can streamline routine analytical tasks and improve method development efficiency.