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

Jung A. Um, Young Geun Choi, Dong Kyu Lee, Yun Sun Lee, Chang Ju Lim, Young A. Youn, Hwa Dong Lee, Hi Jae Cho, Jeong Hill Park, Young Bae Seo, Hsun Chih Kuo, Johan Lim, Tae Jin Yang, Sung Won Kwon, Jeongmi Lee

Research output: Contribution to journalArticle

4 Citations (Scopus)

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. [Figure not available: see fulltext.]

Original languageEnglish (US)
Pages (from-to)7523-7534
Number of pages12
JournalAnalytical and Bioanalytical Chemistry
Volume405
Issue number23
DOIs
StatePublished - Sep 2013

Fingerprint

Paeonia
Metabolomics
Nuclear magnetic resonance spectroscopy
Magnetic Resonance Spectroscopy
Aminobutyrates
Metabolites
Alanine
Quality control
Arginine
Statistical methods
Intergenic DNA
Metabolome
Nuclear magnetic resonance
Chloroplasts
Korea
Least-Squares Analysis
Quality Control
China
DNA
Testing

Keywords

  • Chloroplast intergenic space analysis
  • Constrained least squares method
  • Geographical origin
  • High-resolution melting analysis
  • Metabolomics
  • Nuclear magnetic resonance spectroscopy

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry

Cite this

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. / Um, Jung A.; Choi, Young Geun; Lee, Dong Kyu; Lee, Yun Sun; Lim, Chang Ju; Youn, Young A.; Lee, Hwa Dong; Cho, Hi Jae; Park, Jeong Hill; Seo, Young Bae; Kuo, Hsun Chih; Lim, Johan; Yang, Tae Jin; Kwon, Sung Won; Lee, Jeongmi.

In: Analytical and Bioanalytical Chemistry, Vol. 405, No. 23, 09.2013, p. 7523-7534.

Research output: Contribution to journalArticle

Um, Jung A. ; Choi, Young Geun ; Lee, Dong Kyu ; Lee, Yun Sun ; Lim, Chang Ju ; Youn, Young A. ; Lee, Hwa Dong ; Cho, Hi Jae ; Park, Jeong Hill ; Seo, Young Bae ; Kuo, Hsun Chih ; Lim, Johan ; Yang, Tae Jin ; Kwon, Sung Won ; Lee, Jeongmi. / 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. In: Analytical and Bioanalytical Chemistry. 2013 ; Vol. 405, No. 23. pp. 7523-7534.
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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. [Figure not available: see fulltext.]",
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T1 - Discrimination between genetically identical peony roots from different regions of origin based on 1H-nuclear magnetic resonance spectroscopy-based metabolomics

T2 - Determination of the geographical origins and estimation of the mixing proportions of blended samples

AU - Um, Jung A.

AU - Choi, Young Geun

AU - Lee, Dong Kyu

AU - Lee, Yun Sun

AU - Lim, Chang Ju

AU - Youn, Young A.

AU - Lee, Hwa Dong

AU - Cho, Hi Jae

AU - Park, Jeong Hill

AU - Seo, Young Bae

AU - Kuo, Hsun Chih

AU - Lim, Johan

AU - Yang, Tae Jin

AU - Kwon, Sung Won

AU - Lee, Jeongmi

PY - 2013/9

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N2 - 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. [Figure not available: see fulltext.]

AB - 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. [Figure not available: see fulltext.]

KW - Chloroplast intergenic space analysis

KW - Constrained least squares method

KW - Geographical origin

KW - High-resolution melting analysis

KW - Metabolomics

KW - Nuclear magnetic resonance spectroscopy

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