Advanced SearchSearch Tips
Comparative Analysis of Cultivation Region of Angelica gigas Using a GC-MS-Based Metabolomics Approach
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Comparative Analysis of Cultivation Region of Angelica gigas Using a GC-MS-Based Metabolomics Approach
Jiang, Guibao; Leem, Jae Yoon;
  PDF(new window)
Background: A set of logical criteria that can accurately identify and verify the cultivation region of raw materials is a critical tool for the scientific management of traditional herbal medicine. Methods and Results: Volatile compounds were obtained from 19 and 32 samples of Angelica gigas Nakai cultivated in Korea and China, respectively, by using steam distillation extraction. The metabolites were identified using GC/MS by querying against the NIST reference library. Data binning was performed to normalize the number of variables used in statistical analysis. Multivariate statistical analyses, such as Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) were performed using the SIMCA-P software. Significant variables with a Variable Importance in the Projection (VIP) score higher than 1.0 as obtained through OPLS-DA and those that resulted in p-values less than 0.05 through one-way ANOVA were selected to verify the marker compounds. Among the 19 variables extracted, styrene, -pinene, and -terpinene were selected as markers to indicate the origin of A. gigas. Conclusions: The statistical model developed was suitable for determination of the geographical origin of A. gigas. The cultivation regions of six Korean and eight Chinese A. gigas. samples were predicted using the established OPLS-DA model and it was confirmed that 13 of the 14 samples were accurately classified.
Angelica gigas Nakai;Gas Chromatography-Mass Spectrometer;Metabolomics;Multivariate Statistical Analyses;Orthogonal Partial Least Squares-Discriminant Analysis;Principal Component Analysis;
 Cited by
Arbona V, Iglesias DJ, Talon M and Gomez-Cadenas A. (2009). Plant phenotype demarcation using nontargeted LC-MS and GC-MS metabolite profiling. Journal of Agricultural and Food Chemistry. 57:7338-7347. crossref(new window)

Bylesjo M, Rantalainen M, Cloarec O, Nicholson JK, Holmes E and Trygg J. (2006). OPLS discriminant analysis: Combining the strengths of PLS-DA and SIMCA classification. Journal of Chemometrics. 20:341-351. crossref(new window)

Claudino WM, Goncalves PH, di Leo A, Philip PA and Sarkar FH. (2012). Metabolomics in cancer: A bench-to-bedside intersection. Critical Reviews in Oncology/Hematology. 84:1-7. crossref(new window)

Clevenger JF. (1928). Apparatus for the determination of volatile oil. Journal of American Pharmaceutical Association. 17:345-349.

De Vos RCH, Moco S, Lommen A, Keurentjes JJB, Bino RJ and Hall RD. (2007). Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nature Protocols. 2:778-791. crossref(new window)

European Directorate for the Quality of Medicines and Healthcare(EDQM). (2015). European pharmacopoeia technical guide for the elaboration of minographs. European Directorate for the Quality of Medicines and Healthcare. Strasbourg, France. by 2015 April 20).

Fiehn O. (2002). Metabolomics: The link between genotypes and phenotypes. Plant Molecular Biology. 48:155-171. crossref(new window)

Fukusaki E and Kobayashi A. (2005). Plant metabolomics: Potential for practical operation. Journal of Bioscience and Bioengineering. 100:347-354. crossref(new window)

Hong YS. (2014). Nutritional metabolomics. Journal of the Korean Society of Food Science and Nutrition. 43:179-186. crossref(new window)

Kanani H, Chrysanthopoulos PK and Klapa MI. (2008). Standardizing GC-MS metabolomics. Journal of Chromatography B. 871:191-201. crossref(new window)

Kende A, Portwood D, Senior A, Earll M, Bolygo E and Seymour M. (2010). Target list building for volatile metabolite profiling of fruit. Journal of Chromatography A. 1217:6718-6723. crossref(new window)

Kim EJ, Kwon J, Park SH, Park C, Seo YB, Shin HK, Kim HK, Lee KS, Choi SY, Ryu DH and Hwang GS. (2011). Metabolite profiling of Angelica gigas from different geographical origins using 1H NMR and UPLC-MS analyses. Journal of Agricultural and Food Chemistry. 59:8806-8815. crossref(new window)

Kim MR, Abd El-Aty AM, Kim IS and Shim JH. (2006). Determination of volatile flavor components in danggui cultivars by solvent free injection and hydrodistillation followed by gas chromatographic-mass spectrometric analysis. Journal of Chromatography A. 1116:259-264. crossref(new window)

Lee DK, Yoon MH, Kang YP, Yu J, Park JH, Lee J and Kwon SW. (2013). Comparison of primary and secondary metabolites for suitability to discriminate the origins of Schisandra chinensis by GC/MS and LC/MS. Food Chemistry. 141:3931-3937. crossref(new window)

Lee HW, Choi JH, Park SY, Choo BK, Chun JM, Lee AY and Kim HK. (2008). Constituents comparison of components in native and cultivated species of Angelica tenuissima Nakai. Korean Journal of Medicinal Crop Science. 16:168-172.

Lindon JC, Holmes E and Nicholson JK. (2007). Metabolomics in pharmaceutical R&D. Federation of European Biochemical Societies Journal. 274:1140-1151.

National Institute of Standards and Technology(NIST). (2008). NIST08 mass spectral library(NIST08/2008). National Institute of Standard and Technology. Gaithersburg. MD, USA. by 2011 Nov 20).

Ozek G, Demirci F, Özek T, Tabanca N, Wedge DE, Khan SI, Baser KHC, Duran A and Hamzaoglu E. (2010). Gas chromatographic-mass spectrometric analysis of volatiles obtained by four different techniques from Salvia rosifolia Sm. and evaluation for biological activity. Journal of Chromatography A. 1217:741-748. crossref(new window)

Tianniam S, Tarachiwin L, Bamba T, Kobayashi A and Fukusaki E. (2008). Metabolic profiling of Angelica acutiloba roots utilizing gas chromatography-time-of-flight-mass spectrometry for quality assessment based on cultivation area and cultivar via multivariate pattern recognition. Journal of Bioscience and Bioengineering. 105:655-659. crossref(new window)

Westerhuis JA, van Velzen EJJ, Hoefsloot HCJ and Smilde AK. (2010). Multivariate paired data analysis: Multilevel PLSDA versus OPLSDA. Metabolomics. 6:119-128. crossref(new window)

Wiklund S, Johansson E, Sjstrm L, Mellerowicz EJ, Edlund U, Shockcor JP, Gottfries J, Moritz T and Trygg J. (2008). Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Analytical Chemistry. 80:115-122. crossref(new window)

Yang SO, Lee SW, Kim YO, Lee SW, Kim NH, Choi HK, Jung JY, Lee DH and Shin YS. (2014). Comparative analysis of metabolites in roots of Panax ginseng obtained from different sowing methods. Korean Journal of Medicinal Crop Science. 22:17-22. crossref(new window)

Zhang A, Sun H, Wang Z, Sun W, Wang P and Wang X. (2010). Metabolomics: Towards understanding traditional Chinese medicine. Planta Medica. 76:2026-2035. crossref(new window)