The preopt package for pre-optimization of gradient elutions in high-performance liquid chromatography

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Abstract

A theoretical model is described for quick pre-optimization of binary multistep gradient elutions in liquid chromatography. This model utilizes experimental retention-time data under isocratic elution conditions for different proportions of organic modifier in the mobile phase, and simulates the position of the peaks in the chromatogram by calculation of the average velocity of the peaks through the column. Optimization is done for multistep gradients by means of the simplex algorithm. The results must then be confirmed experimentally. Results are given demonstrating the power and validity of the model in the resolution of complex mixtures. The whole process done on a micro-computer with the PREOPT package usually takes about 30 min, without requiring user participation or chromatographic instrumentation.

References (37)

  • J.C. Berridge

    Trends Anal. Chem.

    (1984)
  • G. D'Agostino et al.

    J. Chromatogr.

    (1985)
  • M.A. Stadalius et al.

    J. Chromatogr.

    (1984)
  • L.R. Snyder et al.

    J. Chromatogr.

    (1979)
  • J.C. Berridge

    Microproc. Microsystems

    (1983)
  • K. Jinno et al.

    J. Chromatogr.

    (1984)
  • J.C. Berridge et al.

    J. Chromatogr.

    (1984)
  • Lu Peichang et al.

    J. Chromatogr.

    (1984)
  • W. Wegscheider et al.

    Anal. Chim. Acta

    (1983)
  • H.J.G. Debets et al.

    Anal. Chim. Acta

    (1983)
  • J.W. Weyland et al.

    Anal. Chim. Acta

    (1983)
  • J.L. Glajch et al.

    J. Chromatogr.

    (1980)
  • L.A. Yarbro et al.

    Anal. Chim. Acta

    (1974)
  • L. de Galan

    Trends Anal. Chem.

    (1985)
  • S.N. Deming et al.

    Adv. Chromatogr.

    (1984)
  • H.J. Issaq

    Adv. Chromatogr.

    (1984)
  • M.W. Watson et al.

    Anal. Chem.

    (1979)
  • R.A. Hartwick et al.

    Anal. Chem.

    (1979)
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