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Analyzing chromatographic data using multilevel modeling.

Paweł Wiczling1

  • 1Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416, Gdańsk, Poland. wiczling@gumed.edu.pl.

Analytical and Bioanalytical Chemistry
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This study introduces a multilevel model to predict analyte retention times in chromatography. The model accounts for analyte properties and experimental conditions, improving prediction accuracy for acids and bases.

Keywords:
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Area of Science:

  • Analytical Chemistry
  • Chromatography
  • Chemometrics

Background:

  • Chromatographic methods coupled with mass spectrometry generate large datasets with inherent analyte clustering.
  • Understanding analyte retention behavior is crucial for method development and data interpretation.

Purpose of the Study:

  • To propose a multilevel model for describing retention time data in gradient chromatography.
  • To incorporate analyte physicochemical properties and experimental parameters into retention time prediction.

Main Methods:

  • Development of a multilevel model incorporating deterministic equations, quantitative structure-retention relationships, and stochastic variability.
  • Implementation of the model in Stan for Bayesian inference using Markov chain Monte Carlo methods.
  • Application to a large set of acids and bases using varied gradient conditions (duration, pH).

Main Results:

  • The multilevel model effectively describes retention time variations across different analytes and experimental conditions.
  • The model integrates analyte properties (hydrophobicity, dissociation constant) and instrument parameters.
  • Bayesian inference provided robust parameter estimation and uncertainty quantification.

Conclusions:

  • Multilevel models offer a powerful framework for analyzing complex chromatographic data with hierarchical structures.
  • The proposed model enhances the understanding and prediction of analyte retention in gradient chromatography.
  • This approach facilitates more accurate data analysis and method optimization in analytical chemistry.