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Discrimination between genetically identical peony roots from different regions of origin based on 1H-nuclear magnetic resonance spectroscopy-based metabolomics: determination of the geographical origins and estimation of the mixing proportions of blended samples

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Abstract

Sixty peony root training samples of the same age were collected from various regions in Korea and China, and their genetic diversity was investigated for 23 chloroplast intergenic space regions. All samples were genetically indistinguishable, indicating that the DNA-based techniques employed were not appropriate for determining the samples’ regions of origin. In contrast, 1H-nuclear magnetic resonance (1H-NMR) spectroscopy-based metabolomics coupled with multivariate statistical analysis revealed a clear difference between the metabolic profiles of the Korean and Chinese samples. Orthogonal projections on the latent structure-discrimination analysis allowed the identification of potential metabolite markers, including γ-aminobutyric acid, arginine, alanine, paeoniflorin, and albiflorin, that could be useful for classifying the samples’ regions of origin. The validity of the discrimination model was tested using the response permutation test and blind prediction test for internal and external validations, respectively. Metabolomic data of 21 blended samples consisting of Korean and Chinese samples mixed at various proportions were also acquired by 1H-NMR analysis. After data preprocessing which was designed to eliminate uncontrolled deviations in the spectral data between the testing and training sets, a new statistical procedure for estimating the mixing proportions of blended samples was established using the constrained least squares method for the first time. The predictive procedure exhibited relatively good predictability (adjusted R 2 = 0.7669), and thus has the potential to be used in the quality control of peony root by providing correct indications for a sample’s geographical origins.

1H-NMR spectroscopy-based metabolomics allowed the discrimination between genetically identical peony roots from different regions of origin and the estimation of the mixing proportions of blended samples

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Acknowledgments

This work was supported by the following grants: the Next-Generation BioGreen 21 Program (No. PJ008202) from the Rural Development Administration, Republic of Korea, the Medicinal Herbs Discrimination Project from the Ministry of Health and Welfare, Republic of Korea, the Yujeonja-Donguibogam project based on Traditional herbs (No. 2012M3A9C4048796), Republic of Korea, and the Basic Science Research Program (No. 2011–0024225) of the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (MEST), Republic of Korea.

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Authors and Affiliations

  1. College of Pharmacy, Seoul National University, Seoul, 151-742, South Korea

    Jung A Um, Dong-Kyu Lee, Chang Ju Lim, Jeong Hill Park & Sung Won Kwon

  2. Department of Plant Science, Plant Genomics and Breeding Institute, and Research Institute for Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University, Seoul, 151-921, South Korea

    Yun Sun Lee & Tae-** Yang

  3. Department of Statistics, Seoul National University, Seoul, 151-742, South Korea

    Young-Geun Choi & Johan Lim

  4. School of Pharmacy, Sungkyunkwan University, Suwon, 440-746, South Korea

    Young A Youn & Jeongmi Lee

  5. Korea Promotion Institute for Traditional Medicine Industry, Gyeongsan, 712-260, South Korea

    Hwa Dong Lee & Hi Jae Cho

  6. Department of Herbalogy, College of Oriental Medicine, Daejeon University, Daejeon, 300-716, South Korea

    Young Bae Seo

  7. Department of Statistics, National Chengchi University, No.64, Sec.2, ZhiNan Road, Wenshan District, Taipei, 11605, Taiwan

    Hsun-chih Kuo

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Um, J.A., Choi, YG., Lee, DK. et al. Discrimination between genetically identical peony roots from different regions of origin based on 1H-nuclear magnetic resonance spectroscopy-based metabolomics: determination of the geographical origins and estimation of the mixing proportions of blended samples. Anal Bioanal Chem 405, 7523–7534 (2013). https://doi.org/10.1007/s00216-013-7182-9

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