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Fuzzy clustering algorithms for identification of Exocarpium Citrus Grandis through chromatography

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Abstract

Chromatography has been extensively applied in identification and quality control of Chinese medicines (CMs). However, regular analytical methods are not suitable if labeled patterns or reference patterns are not available. Unsupervised and semi-supervised recognition approaches for chromatographic patterns, namely nonrandomized fuzzy C-Means clustering (FCM) with weighted principal components (NWPC-FCM) and partial supervised FCM with weighted PCs (PSWPC-FCM) are proposed in this work. The basic ideas of the proposed algorithms are as follows: PCs are extracted and weighted according to corresponding variances via principal component analysis to search for more complicated geometry of fuzzy clusters, then nonrandomized methodology and partial supervised clustering with seeds are employed, respectively, in NWPC-FCM and PSWPC-FCM to determine initial cluster centers for reliable cluster results. Satisfactory results were achieved with this method in identification of Exocarpium Citrus Grandis, a genuine herbal medicine of Guangdong Province. The presented algorithms improve cluster effectiveness and reliability significantly compared with standard FCM, PC-FCM, and two widely utilized clustering methods on chromatographic analysis. The research indicates the proposed algorithms exhibit functional applicability and interpretability for pattern recognition in chromatographic fingerprints of CMs in the presence of limited labeling or reference information.

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Acknowledgments

We are thankful to the financial support provided by 2013 National Undergraduate Innovation and Venture Training Project (20131057201), the National Natural Science Foundation of China (81274003), National Key Technology Support Program of China during the Twelfth Five-Year Plan Period by the Ministry of Science and Technology (2011BAI01B02), Guangzhou Zhujiang Science and Technology Future Fellow Fund(2012J2200094). A special honor goes to my doctoral instructor who provided practical advisement throughout completion of this research work. Thank you Professor Zhifeng Hao.

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Authors and Affiliations

  1. School of Computer and Engineering, South China University of Technology, Guangzhou, China

    Hang Wei & Yangyang Xu

  2. School of Chinese MaterialMedical, Guangzhou University of Chinese Medicine, Guangzhou, China

    Li Lin

  3. School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China

    Hang Wei, Honglai Zhang, Jiajing Yu, Jiaming Hong & Qinqun Chen

  4. Department of Computer Science, Guangzhou University, Guangzhou, China

    Rui Chen

  5. Gangdong Medical University, Guangzhou, China

    Shaodong Deng

Authors
  1. Hang Wei
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  2. Li Lin
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  3. Honglai Zhang
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  4. Yangyang Xu
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  5. Shaodong Deng
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  6. Jiajing Yu
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  7. Jiaming Hong
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  8. Rui Chen
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  9. Qinqun Chen
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Corresponding author

Correspondence to Qinqun Chen.

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The authors declare that they have no conflict of interest.

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Communicated by V. Loia.

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Wei, H., Lin, L., Zhang, H. et al. Fuzzy clustering algorithms for identification of Exocarpium Citrus Grandis through chromatography. Soft Comput 21, 1291–1300 (2017). https://doi.org/10.1007/s00500-015-1861-8

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  • DOI: https://doi.org/10.1007/s00500-015-1861-8

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