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Retention modeling and simultaneous optimization of pH value and gradient steepness in RP-HPLC using feed-forward neural networks by Yichu Shan; Ruihuan Zhao; Yukui Zhang; Weibing Zhang; Yan Tian is a Chemistry article available to read on EtoBox.

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## Abstract A novel approach is proposed for the simultaneous optimization of mobile phase pH and gradient steepness in RP‐HPLC using artificial neural networks. By presetting the initial and final concentration of the organic solvent, a limited number of experiments with different gradient time and pH value of mobile phase are arranged in the two‐dimensional space of mobile phase parameters. The retention behavior of each solute is modeled using an individual artificial neural network. An “early stopping” strategy is adopted to ensure the predicting capability of neural networks. The trained neural networks can be used to predict the retention time of solutes under arbitrary mobile phase conditions in the optimization region. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for amino acids derivatised by a new fluorescent reagent.

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Author

Yichu Shan; Ruihuan Zhao; Yukui Zhang; Weibing Zhang; Yan Tian

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Ltd.; Wiley (ISSN 1615-9306)

Published

2003

Language

EN

Field

Chemistry (Physical Sciences)