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Determination of multiple elements in samples of the medicinal plant Marsdenia tenacissima and estimation of geographic origin via pattern recognition techniques

Journal of Natural Medicines volume 69, pages 55–62 (2015)Cite this article

Abstract

Multi-element analysis of the medicinal plant Marsdenia tenacissima was used to develop a reliable method of tracing the geographical source of the samples. The concentrations of 27 elements in 128 samples from 4 provinces in China were analyzed by inductively coupled plasma–atomic emission spectroscopy. Pattern recognition techniques, viz. principal component analysis (PCA), cluster analysis (CA), stepwise linear discriminant analysis (SLDA) and k-nearest neighbor analysis (KNN), were used for this purpose. It was verified that 21 elements in the M. tenacissima samples from different regions showed significant differences (P < 0.05). The PCA explained 87.36 % of the variance with the first seven principal component variables, and a score plot produced from the largest three principal components showed that the source area of most samples could be correctly distinguished. The CA showed that samples were separated into three clusters. The SLDA produced an overall correct classification rate of 87.5 % and a cross-validation rate of 85.2 %. The KNN analysis performed ideally, with an average identification rate of 100 % for the training set and 93.33 % for the test set. These results laid the foundation for the application of multi-element analysis combined with pattern recognition techniques for tracing the geographical origin of samples of medicinal plants.

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References

  1. The State Pharmacopoeia Commission of P. R. China (2010) Pharmacopoeia of the P. R. China. vol I. China Medical Science Press, Beijing, pp 277–278

  2. Zhang H, Tan AM, Zhang AY, Chen R, Yang SB, Huang X (2010) Five new C-21 steroidal glycosides from the stems of Marsdenia tenacissima. Steroids 75:176–183

    CAS  PubMed  Article  Google Scholar 

  3. Deng J, Liao ZX, Chen DF (2005) Marsdenosides A-H, polyoxypregnane glycosides from Marsdenia tenacissima. Phytochemistry 66:1040–1051

    CAS  PubMed  Article  Google Scholar 

  4. Li QF, Wang XL, Ding LS, Zhang C (2007) Polyoxypregnanes from the stems of Marsdenia tenacissima. Chinese Chem Lett 18:831–834

    CAS  Article  Google Scholar 

  5. Han SY, Zhao MB, Zhuang GB, Li PP (2012) Marsdenia tenacissima extract restored gefitinib sensitivity in resistant non-small cell lung cancer cells. Lung Cancer 75:30–37

    PubMed  Article  Google Scholar 

  6. Zhang MZ, He Z, Wu GY, Xing Z, Song M, Wang R (2008) Effect of xiaoaiping injection on Ec-9706 cell line. Lishizhen Med Mater Med Res 19:1182–1184

    Google Scholar 

  7. Schwägele F (2005) Traceability from a European perspective. Meat Sci 71(1):164–173

    PubMed  Article  Google Scholar 

  8. Cheng PY, Fan WL, Xu Y (2014) Determination of Chinese liquors from different geographic origins by combination of mass spectrometry and chemometric technique. Food Control 35:153–158

    CAS  Article  Google Scholar 

  9. Martin AE, Watling RJ, Lee GS (2012) The multi-element determination and regional discrimination of Australian wines. Food Chem 133:1081–1089

    CAS  Article  Google Scholar 

  10. Yücel Y, Sultanoğlu P (2013) Characterization of Hatay honeys according to their multi-element analysis using ICP-OES combined with chemometrics. Food Chem 140:231–237

    PubMed  Article  Google Scholar 

  11. Moreda-Piñeiro A, Fisher A, Hill SJ (2003) The classification of tea according to region of origin using pattern recognition techniques and trace metal data. J Food Compos Anal 16:195–211

    Article  Google Scholar 

  12. Zhao HY, Guo BL, Wei YM, Zhang B (2013) Multi-element composition of wheat grain and provenance soil and their potentialities as fingerprints of geographical origin. J Cereal Sci 57:391–397

    CAS  Article  Google Scholar 

  13. Li G, Nunes L, Wang Y, Williams PN, Zheng MZ, Zhang QF, Zhu Y (2013) Profiling the ionome of rice and its use in discriminating geographical origins at the regional scale, China. J Environ Sci 25:144–154

    Article  Google Scholar 

  14. Sun SM, Guo BL, Wei YM, Fan MT (2011) Multi-element analysis for determining the geographical origin of mutton from different regions of China. Food Chem 124:1151–1156

    CAS  Article  Google Scholar 

  15. Liu XF, Xue CH, Wang YM, Li ZJ, Xue Y, Xu J (2012) The classification of sea cucumber (Apostichopus japonicus) according to region of origin using multi-element analysis and pattern recognition techniques. Food Control 23:522–527

    CAS  Article  Google Scholar 

  16. Benincasa C, Lewis J, Perri E, Sindona G, Tagarelli A (2007) Determination of trace element in Italian virgin olive oils and their characterization according to geographical origin by statistical analysis. Anal Chim Acta 585:366–370

    CAS  PubMed  Article  Google Scholar 

  17. Daultrey S (1976) Principal components analysis. Geo Abstracts Limited Norwich

  18. Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis. Wiley, New York

    Google Scholar 

  19. Izenman AJ (2008) Linear discriminant analysis. Springer, Berlin

    Google Scholar 

  20. Weinberger KQ, Saul LK (2009) Distance metric learning for large margin nearest neighbor classification. J Mach Learn Res 10:207–244

    Google Scholar 

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Acknowledgment

Yunnan Xintong Botanic Pharmaceutical Co., Ltd., Mengzi 661100, China.

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The authors declare that there are no conflicts of interest regarding the publication of this article.

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Affiliations

  1. Institute of Chinese Medicinal Materials, Nanjing Agricultural University, 210095, Nanjing, China

    Chao Li, Qiao-sheng Guo & Kai-yan Zheng

  2. Institute of Chinese Medicinal Materials, Yunnan Agricultural University, 650201, Kunming, China

    Sheng-chao Yang, Zhen-gui Meng & Xiang-zeng Xu

  3. Yunnan Xintong Botanic Pharmaceutical Co., Ltd, 661100, Mengzi, China

    Wang-ping Li

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  1. Chao Li
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  2. Qiao-sheng Guo
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  3. Sheng-chao Yang
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  4. Kai-yan Zheng
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  5. Wang-ping Li
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  6. Zhen-gui Meng
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  7. Xiang-zeng Xu
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Correspondence to Qiao-sheng Guo or Sheng-chao Yang.

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Li, C., Guo, Qs., Yang, Sc. et al. Determination of multiple elements in samples of the medicinal plant Marsdenia tenacissima and estimation of geographic origin via pattern recognition techniques. J Nat Med 69, 55–62 (2015). https://doi.org/10.1007/s11418-014-0860-x

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  • DOI: https://doi.org/10.1007/s11418-014-0860-x

Keywords

  • Marsdenia tenacissima
  • Geographical origin
  • Pattern recognition techniques
  • Multi-element analysis