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Optimization methods in chromatography and capillary electrophoresis.

A M Siouffi1, R Phan-Tan-Luu

  • 1Faculté des Sciences de St. Jérôme, Université Aix-Marseille III, France.

Journal of Chromatography. A
|October 25, 2000
PubMed
Summary
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Chemometric methods optimize chromatography and capillary electrophoresis parameters by varying factors together. While artificial neural networks are increasingly used, regression methods are simpler when models are available.

Area of Science:

  • Analytical Chemistry
  • Separation Science

Background:

  • Optimization of parameters is crucial for chromatography and capillary electrophoresis.
  • Chemometric approaches involve simultaneous variation of experimental factors.
  • Traditional methods like simplex and overlapping resolution maps are declining in popularity.

Purpose of the Study:

  • To review and compare various optimization methods in chromatography and capillary electrophoresis.
  • To highlight the growing importance of chemometrics and artificial neural networks.
  • To discuss the advantages and limitations of different optimization strategies.

Main Methods:

  • Chemometric approaches utilizing objective functions for criteria like selectivity and resolution.
  • Design of Experiments (DOE) methods, including factorial and central composite designs, particularly for electrodriven capillary separations.

Related Experiment Videos

  • Artificial Neural Networks (ANNs) for complex optimization tasks.
  • Regression methods when explicit models are available.
  • Linear Solvation Energy Relationships (LSERs) as a valuable tool.

    • Main Results:

    • Factorial and central composite designs are gaining popularity in capillary electrophoresis due to a larger number of parameters.
    • Artificial neural networks show increasing application in method optimization.
    • Computer-assisted optimization methods are well-established in Reversed-Phase Liquid Chromatography (RPLC) but are nascent in Capillary Electrophoresis (CE).
    • While chemometrics offer model-free optimization, they can require numerous experiments and complex domain definition.
    • Regression methods simplify optimization when models are available.

    Conclusions:

    • Chemometric tools offer flexibility but can be experiment-intensive.
    • DOE and ANNs are becoming increasingly vital for optimizing complex separation methods like CE.
    • Further development is needed for computer-assisted optimization and LSER coefficient estimation in CE.
    • The choice of optimization method depends on the availability of models and the complexity of the separation system.