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The Robust Models of Retention for Thin Layer Chromatography

  • Miron B. Kursa
  • Łukasz Komsta
  • Witold R. Rudnicki
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 103)

Abstract

We present an application of the machine learning methods for modelling the retention constants in the thin layer chromatography. First a feature selection algorithm is applied to reduce the feature space and then the regression models are built with a help of the random forest algorithm. The models obtained in this way have better correlation with the experimental data than the reference models built with linear regression. They are also robust—the cross-validation tests shows that the accuracy on unseen data is on average identical to the cross-validated accuracy obtained on the training set.

Keywords

random forest feature selection thin layer chromatography 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Miron B. Kursa
    • 1
  • Łukasz Komsta
    • 2
  • Witold R. Rudnicki
    • 1
  1. 1.Interdisciplinary Centre for Mathematical and Computational ModellingUniversity of WarsawWarsawPoland
  2. 2.Department of Medicinal ChemistrySkubiszewski Medical UniversityLublinPoland

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