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Prediction of Chromatographic Retention of Pyrazine and Alkylpyrazines in RP-LC

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Abstract

Retention prediction models for a group of pyrazines chromatographed under reversed-phase mode were developed using multiple linear regression (MLR) and artificial neural networks (ANNs). Using MLR, the retention of the analytes were satisfactorily described by a two-predictor model based on the logarithm of the partition coefficient of the analytes (log P) and the percentage of the organic modifier in the mobile phase (ACN or MeOH). ANN prediction models were also derived using the predictors derived from MLR as inputs and log k as outputs. The best network architecture was found to be 2-2-1 for both ACN and MeOH data sets. The optimized ANNs showed better predictive properties than the MLR models especially for the ACN data set. In the case of the MeOH data set, the MLR and ANN models have comparable predictive performance.

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Acknowledgments

The authors would like to thank Morinaga & Co., Ltd., Yokohama, Japan for the financial support. One of the authors, KY, also acknowledges Mr. N. Ookawara, Mr. K. Sugiyama and all the members in the laboratory of food analysis (Morinaga & Co., Ltd.) for their valuable help and suggestions.

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Authors and Affiliations

  1. School of Materials Science, Toyohashi University of Technology, Toyohashi, 441-8580, Japan

    Kentaro Yogo, Yoshihiro Saito & Kiyokatsu Jinno

  2. Department of Physical Sciences and Mathematics, College of Arts and Sciences, University of the Philippines Manila, 1000, Manila, Philippines

    Noel S. Quiming

Authors
  1. Kentaro Yogo
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  2. Noel S. Quiming
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  3. Yoshihiro Saito
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  4. Kiyokatsu Jinno
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Corresponding author

Correspondence to Yoshihiro Saito.

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Yogo, K., Quiming, N.S., Saito, Y. et al. Prediction of Chromatographic Retention of Pyrazine and Alkylpyrazines in RP-LC. Chroma 70, 677–684 (2009). https://doi.org/10.1365/s10337-009-1243-4

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  • DOI: https://doi.org/10.1365/s10337-009-1243-4

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