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Functional Genome of Medicinal Plants

  • Jian Yang
  • Meirong Jia
  • Juan GuoEmail author
Chapter

Abstract

As a complex organism, research on the origin, evolution, development, physiology, and genetic traits of medicinal plants has shown strong development prospects for medicinal plant functional genomics. Functional genomics is a science based on genome sequence information, which uses various genomic techniques to organically link genome sequences with gene functions (including gene networks) and phenotypes at the system level and ultimately reveal the functions of biological systems at different levels in nature (genome, transcriptome, proteome, metabolome, epigenome, etc.). Research into the genomes, transcriptomes, and proteomes of medicinal plants provides a premise and basis for comprehensively analysing various life phenomena at the molecular level. Combined with metabolomic research, it greatly promotes the application of frontier life science and technology in the field of medicinal plants, lays the foundation for clarifying the synthesis and regulation of effective components of medicinal plants, and promotes research on the interaction between genetic and environmental factors of medicinal plants.

References

  1. 1.
    Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci. 1977;74(12):5463–7.PubMedCrossRefPubMedCentralGoogle Scholar
  2. 2.
    Maxam AM, Gilbert W. A new method for sequencing DNA. Proc Natl Acad Sci. 1977;74(2):560–4.PubMedCrossRefPubMedCentralGoogle Scholar
  3. 3.
    Feng C, et al. Assessing performance of orthology detection strategies applied to eukaryotic genomes. PLoS One. 2007;2(4):e383.CrossRefGoogle Scholar
  4. 4.
    Pan Z, et al. Reviews in comparative genomic research based on orthologs. Hereditas. 2009;31(5):457–63.PubMedCrossRefPubMedCentralGoogle Scholar
  5. 5.
    Ohta T. Evolution by gene duplication revisited: differentiation of regulatory elements versus proteins. Genetica. 2003;118(2–3):209–16.PubMedCrossRefPubMedCentralGoogle Scholar
  6. 6.
    Hakes L, et al. All duplicates are not equal: the difference between small-scale and genome duplication. Genome Biol. 2007;8(10):1–13.CrossRefGoogle Scholar
  7. 7.
    Li X, et al. Origin and evolution of new genes. Chin Sci Bull. 2004;49(13):1219–25.CrossRefGoogle Scholar
  8. 8.
    Zhang J. Evolution by gene duplication: an update. Trends Ecol Evol. 2003;18(6):292–8.CrossRefGoogle Scholar
  9. 9.
    Edger PP, Pires JC. Gene and genome duplications: the impact of dosage-sensitivity on the fate of nuclear genes. Chromosom Res Int J Mol Supramol Evol Asp Chromosom Biol. 2009;17(5):699.CrossRefGoogle Scholar
  10. 10.
    Sun H, Ge S. Review of the evolution of duplicated genes. Chin Bull Bot. 2010;45(1):13–22.Google Scholar
  11. 11.
    Xu J, et al. Panax ginseng genome examination for ginsenoside biosynthesis. GigaScience. 2017;6(11): gix093–gix093.Google Scholar
  12. 12.
    Xu H, et al. Analysis of the genome sequence of the medicinal plant Salvia miltiorrhiza. Mol Plant. 2016;9(6):949–52.PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Shen Q, et al. The genome of Artemisia annua provides insight into the evolution of Asteraceae family and artemisinin biosynthesis. Mol Plant. 2018;11(6):776–88.PubMedCrossRefPubMedCentralGoogle Scholar
  14. 14.
    Mochida K, et al. Draft genome assembly and annotation of Glycyrrhiza uralensis, a medicinal legume. Plant J. 2017;89(2):181–94.PubMedCrossRefPubMedCentralGoogle Scholar
  15. 15.
    Yan L, et al. The genome of Dendrobium officinale illuminates the biology of the important traditional Chinese orchid herb. Mol Plant. 2015;8(6):922–34.PubMedCrossRefPubMedCentralGoogle Scholar
  16. 16.
    Guo L, et al. The opium poppy genome and morphinan production. Science. 2018;362(6412):343–7.PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    He S, et al. MicroRNA-encoding long non-coding RNAs. BMC Genomics. 2008;9(1):1–11.CrossRefGoogle Scholar
  18. 18.
    Mattick JS. The functional genomics of noncoding RNA. Science. 2005;309(5740):1527–8.PubMedCrossRefPubMedCentralGoogle Scholar
  19. 19.
    Blackstock WP, Weir MP. Proteomics: quantitative and physical mapping of cellular proteins. Trends Biotechnol. 1999;17(3):121–7.PubMedCrossRefPubMedCentralGoogle Scholar
  20. 20.
    González-Díaz H, et al. Proteomics, networks and connectivity indices. Proteomics. 2008;8(4):750–78.PubMedCrossRefPubMedCentralGoogle Scholar
  21. 21.
    Gao W, et al. Combining metabolomics and transcriptomics to characterize tanshinone biosynthesis in Salvia miltiorrhiza. BMC Genomics. 2014;15(1):73.PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    Guo J, et al. CYP76AH1 catalyzes turnover of miltiradiene in tanshinones biosynthesis and enables heterologous production of ferruginol in yeasts. Proc Natl Acad Sci. 2013;110(29):12108–13.PubMedCrossRefPubMedCentralGoogle Scholar
  23. 23.
    Guo J, et al. Cytochrome P450 promiscuity leads to a bifurcating biosynthetic pathway for tanshinones. New Phytol. 2016;210(2):525–34.PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Lei Y, et al. Transcriptome analysis of medicinal plant Salvia miltiorrhiza and identification of genes related to Tanshinone biosynthesis. PLoS One. 2013;8(11):e80464.CrossRefGoogle Scholar
  25. 25.
    Van Someren EP, et al. Genetic network modeling. Pharmacogenomics. 2002;3(4):507–25.PubMedCrossRefPubMedCentralGoogle Scholar
  26. 26.
    Alex VM, et al. CathaCyc, a metabolic pathway database built from Catharanthus roseus RNA-Seq data. Plant Cell Physiol. 2013;54(5):673–85.CrossRefGoogle Scholar
  27. 27.
    Ma Y-N, et al. Jasmonate promotes artemisinin biosynthesis by activating the TCP14-ORA complex in Artemisia annua. Sci Adv. 2018;4(11):eaas9357.PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Yu Z-X, et al. The jasmonate-responsive AP2/ERF transcription factors AaERF1 and AaERF2 positively regulate artemisinin biosynthesis in Artemisia annua L. Mol Plant. 2012;5(2):353–65.CrossRefGoogle Scholar
  29. 29.
    Shen Q, et al. The jasmonate-responsive AaMYC2 transcription factor positively regulates artemisinin biosynthesis in Artemisia annua. New Phytol. 2016;210(4):1269–81.PubMedCrossRefPubMedCentralGoogle Scholar
  30. 30.
    Tang Y, et al. AaEIN3 mediates the downregulation of artemisinin biosynthesis by ethylene signaling through promoting leaf senescence in Artemisia annua. Front Plant Sci. 2018;9:413.PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Yan T, et al. HOMEODOMAIN PROTEIN 1 is required for jasmonate-mediated glandular trichome initiation in Artemisia annua. New Phytol. 2017;213(3):1145–55.PubMedCrossRefGoogle Scholar
  32. 32.
    Qi J, et al. Mining genes involved in the stratification of Paris Polyphylla seeds using high-throughput embryo transcriptome sequencing. BMC Genomics. 2013;14(1): 358–358.PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Simon SA, et al. Short-read sequencing technologies for transcriptional analyses. Annu Rev Plant Biol. 2009;60(1):305.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Gai S, et al. Transcriptome analysis of tree peony during chilling requirement fulfillment: assembling, annotation and markers discovering. Gene. 2012;497(2):256–62.PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Jain A, Chaudhary S, Sharma PC. Mining of microsatellites using next generation sequencing of seabuckthorn (Hippophae rhamnoides L.) transcriptome. Physiol Mol Biol Plant. 2014;20(1):115–23.CrossRefGoogle Scholar
  36. 36.
    Lin W, et al. Transcriptome analysis of Houttuynia cordata Thunb. by Illumina paired-end RNA sequencing and SSR marker discovery. PLoS One. 2014;9(1):e84105.CrossRefGoogle Scholar
  37. 37.
    Zeng S, et al. Development of a EST dataset and characterization of EST-SSRs in a traditional Chinese medicinal plant, Epimedium sagittatum (Sieb. Et Zucc.) Maxim. BMC Genomics. 2010;11:94.PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Wang Y-D, Wang X, Wong Y-s. Proteomics analysis reveals multiple regulatory mechanisms in response to selenium in rice. J Proteome. 2012;75(6):1849–66.CrossRefGoogle Scholar
  39. 39.
    Schmid MB. Structural proteomics: the potential of high-throughput structure determination. Trends Microbiol. 2002;10(10):s27–31.PubMedCrossRefPubMedCentralGoogle Scholar
  40. 40.
    Aggarwal K, Lee HK. Functional genomics and proteomics as a foundation for systems biology. Brief Funct Genomics. 2003;2(3):175–84.CrossRefGoogle Scholar
  41. 41.
    Lesley SA, et al. Structural genomics of the Thermotoga maritima proteome implemented in a high-throughput structure determination pipeline. Proc Natl Acad Sci. 2002;99(18):11664–9.PubMedCrossRefGoogle Scholar
  42. 42.
    Zhu W, et al. Variations of metabolites and proteome in Lonicera japonica Thunb. Buds and flowers under UV radiation. Biochim Biophys Acta (BBA) – Proteins Proteomics. 2017;1865(4):404–13.CrossRefGoogle Scholar
  43. 43.
    Zhu W, et al. Binary stress induces an increase in indole alkaloid biosynthesis in Catharanthus roseus. Front Plant Sci. 2015;6:582.PubMedPubMedCentralGoogle Scholar
  44. 44.
    Wang Y, et al. Comparative proteomic analysis of the response to silver ions and yeast extract in Salvia miltiorrhiza hairy root cultures. Plant Physiol Biochem. 2016;107:364–73.PubMedCrossRefGoogle Scholar
  45. 45.
    Adrian B. Perceptions of epigenetics. Nature. 2007;447(7143):396–8.CrossRefGoogle Scholar
  46. 46.
    Jablonka E, Lamb MJ. The changing concept of epigenetics. Ann N Y Acad Sci. 2010;981(1):82–96.CrossRefGoogle Scholar
  47. 47.
    Finnegan EJ, et al. DNA methylation in plants. Annu Rev Plant Physiol Plant Mol Biol. 1998;49(1):223–47.PubMedCrossRefGoogle Scholar
  48. 48.
    Loïc P, Wen-Hsiung L. Evolutionary diversification of DNA methyltransferases in eukaryotic genomes. Mol Biol Evol. 2005;22(4):1119–28.CrossRefGoogle Scholar
  49. 49.
    Chan S, Henderson I, Jacobsen S. Gardening the genome: DNA methylation in Arabidopsis thaliana. Nat Rev Genet. 2005;6(5):351–60.PubMedCrossRefGoogle Scholar
  50. 50.
    Bernard A, Emilie C, Rachel M. Environmentally induced phenotypes and DNA methylation: how to deal with unpredictable conditions until the next generation and after. Mol Ecol. 2010;19(7):1283–95.CrossRefGoogle Scholar
  51. 51.
    Chiang PK, et al. S-Adenosylmethionine and methylation. FASEB J Off Publ Fed Am Soc Exp Biol. 1996;10(4):471.Google Scholar
  52. 52.
    Turner BM. Histone acetylation and an epigenetic code. BioEssays. 2000;22(9):836–45.PubMedCrossRefPubMedCentralGoogle Scholar
  53. 53.
    Jenuwein T, Allis CD. Translating the histone code. Science. 2001;293(5532):1074–80.PubMedCrossRefPubMedCentralGoogle Scholar
  54. 54.
    Tian L, et al. Reversible histone acetylation and deacetylation mediate genome-wide, promoter-dependent, and locus-specific changes in gene expression during plant development. Genetics. 2004;169(1):337–45.PubMedCrossRefPubMedCentralGoogle Scholar
  55. 55.
    Tariq M, Paszkowski J. DNA and histone methylation in plants. Trends Genet. 2004;20(6):244–51.PubMedCrossRefPubMedCentralGoogle Scholar
  56. 56.
    Lee RC, Feinbaum RL, Ambros VR. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993;75(5):843–54.PubMedCrossRefPubMedCentralGoogle Scholar
  57. 57.
    Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116(2):281–97.PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Kidner CA, Martienssen RA. The developmental role of microRNA in plants. Curr Opin Plant Biol. 2005;8(1):38–44.PubMedCrossRefPubMedCentralGoogle Scholar
  59. 59.
    Hammond SM, et al. An RNA-directed nuclease mediates post-transcriptional gene silencing in Drosophila cells. Nature. 2000;404(6775):293–6.PubMedCrossRefPubMedCentralGoogle Scholar
  60. 60.
    Zamore PD, et al. RNAi: double-stranded RNA directs the ATP-dependent cleavage of mRNA at 21 to 23 nucleotide intervals. Cell. 2000;101(1):25–33.PubMedCrossRefPubMedCentralGoogle Scholar
  61. 61.
    Flatscher, R, et al. Environmental heterogeneity and phenotypic divergence: can heritable epigenetic variation aid speciation? Genet Res Int. 2012;2012:698421–698421.CrossRefGoogle Scholar
  62. 62.
    Rollins RA, et al. Large-scale structure of genomic methylation patterns. Genome Res. 2005;16(2):157–63.PubMedCrossRefPubMedCentralGoogle Scholar
  63. 63.
    Frommer M, et al. A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Natl Acad Sci U S A. 1992;89(5):1827–31.PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    Cokus SJ, et al. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature. 2008;452(7184):215–9.PubMedPubMedCentralCrossRefGoogle Scholar
  65. 65.
    Nair SS, et al. Comparison of methyl-DNA immunoprecipitation (MeDIP) and methyl-CpG binding domain (MBD) protein capture for genome-wide DNA methylation analysis reveal CpG sequence coverage bias. Epigenetics. 2011;6(1):34–44.PubMedCrossRefPubMedCentralGoogle Scholar
  66. 66.
    Wojdacz TK, Alexander D. Methylation-sensitive high resolution melting (MS-HRM): a new approach for sensitive and high-throughput assessment of methylation. Nucleic Acids Res. 2007;35(6):e41.PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Oneill LP, Turner BM. Immunoprecipitation of chromatin. Methods Enzymol. 1996;274:189–97.CrossRefGoogle Scholar
  68. 68.
    Miura H, Tomaru Y. ChIP on chip for transcriptional regulatory network analysis. Tanpakushitsu Kakusan Koso Protein Nucleic Acid Enzyme. 2004;49(17 Suppl):2710.Google Scholar
  69. 69.
    Nix DA, Courdy SJ, Boucher KM. Empirical methods for controlling false positives and estimating confidence in ChIP-Seq peaks. BMC Bioinf. 2008;9(1):1–9.CrossRefGoogle Scholar
  70. 70.
    Shi R, Chiang VL. Facile means for quantifying microRNA expression by real-time PCR. BioTechniques. 2005;39(4):519–25.PubMedCrossRefPubMedCentralGoogle Scholar
  71. 71.
    Mestdagh P, et al. High-throughput stem-loop RT-qPCR miRNA expression profiling using minute amounts of input RNA. Nucleic Acids Res. 2008;36(21):e143.PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Liu CG, et al. An oligonucleotide microchip for genome-wide microRNA profiling in human and mouse tissues. Proc Natl Acad Sci U S A. 2004;101(26):9740–4.PubMedPubMedCentralCrossRefGoogle Scholar
  73. 73.
    Ni Z, et al. Effects of 5-azacytidine on bioactive components of Dendrobium. J Zhejiang Agric Sci. 2014;7:1018–20.Google Scholar
  74. 74.
    Li C, et al. Transcriptome analysis reveals ginsenosides biosynthetic genes, microRNAs and simple sequence repeats in Panax ginsengC. A. Meyer. BMC Genomics. 2013;14(1):245.PubMedPubMedCentralCrossRefGoogle Scholar
  75. 75.
    Vashisht I, et al. Mining NGS transcriptomes for miRNAs and dissecting their role in regulating growth, development, and secondary metabolites production in different organs of a medicinal herb, Picrorhiza kurroa. Planta. 2015;241(5):1255–68.PubMedCrossRefPubMedCentralGoogle Scholar
  76. 76.
    Zhang Y, Chu H, Zhang J. Comparison of population genetic and epigenetic diversity of Salvia miltiorrhiza in Qinba Mountains. Acta Agric Boreali-Occiden Sin. 2012;10:142–8.Google Scholar
  77. 77.
    Boyko A, et al. Transgenerational adaptation of Arabidopsis to stress requires DNA methylation and the function of dicer-like proteins. PLoS One. 2010;5(3):e9514.PubMedPubMedCentralCrossRefGoogle Scholar
  78. 78.
    Li M-R, et al. Genetic and epigenetic diversities shed light on domestication of cultivated ginseng (Panax ginseng). Mol Plant. 2015;8(11):1612–22.PubMedCrossRefPubMedCentralGoogle Scholar
  79. 79.
    Baek D, et al. Regulated AtHKT1 gene expression by a distal enhancer element and DNA methylation in the promoter plays an important role in salt tolerance. Plant Cell Physiol. 2011;52(1):149–61.PubMedCrossRefPubMedCentralGoogle Scholar
  80. 80.
    Huang W, et al. SlAGO4A, a core factor of RNA-directed DNA methylation (RdDM) pathway, plays an important role under salt and drought stress in tomato. Mol Breed. 2016;36(3):28.CrossRefGoogle Scholar
  81. 81.
    Villas-Boas SG, et al. Metabolome analysis: an introduction, vol. 24. Hoboken: John Wiley & Sons; 2007.CrossRefGoogle Scholar
  82. 82.
    Fiehn O. Metabolomics – the link between genotypes and phenotypes. Plant Mol Biol. 2002;48(1–2):155–71.PubMedCrossRefPubMedCentralGoogle Scholar
  83. 83.
    Fukusaki E, Kobayashi A. Plant metabolomics: potential for practical operation. J Biosci Bioeng. 2005;100(4):347–54.PubMedCrossRefPubMedCentralGoogle Scholar
  84. 84.
    Trethewey RN. Metabolite profiling as an aid to metabolic engineering in plants. Curr Opin Plant Biol. 2004;7(2):196–201.CrossRefGoogle Scholar
  85. 85.
    Hirai MY, et al. Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proc Natl Acad Sci U S A. 2004;101(27):10205–10.PubMedPubMedCentralCrossRefGoogle Scholar
  86. 86.
    Duan L, et al. Use of the metabolomics approach to characterize Chinese medicinal material Huangqi. Mol Plant. 2012;5(2):376–86.PubMedCrossRefPubMedCentralGoogle Scholar
  87. 87.
    Xie G, et al. Ultra-performance LC/TOF MS analysis of medicinal Panax herbs for metabolomic research. J Sep Sci. 2008;31:1015–26.PubMedCrossRefPubMedCentralGoogle Scholar
  88. 88.
    Wang L, Wang X, Kong L. Automatic authentication and distinction of Epimedium koreanum and Epimedium wushanense with HPLC fingerprint analysis assisted by pattern recognition techniques. Biochem Syst Ecol. 2012;40:138–45.CrossRefGoogle Scholar
  89. 89.
    Sun H, et al. UPLC–Q-TOF–HDMS analysis of constituents in the root of two kinds of aconitum using a metabolomics approach. Phytochem Anal. 2013;24(3):263–76.CrossRefGoogle Scholar
  90. 90.
    Liu NQ, et al. Metabolomic investigation of the ethnopharmacological use of Artemisia afra with NMR spectroscopy and multivariate data analysis. J Ethnopharmacol. 2010;128(1):230–5.PubMedCrossRefPubMedCentralGoogle Scholar
  91. 91.
    Lu G, et al. Development of high-performance liquid chromatographic fingerprints for distinguishing Chinese Angelica from related umbelliferae herbs. J Chromatogr A. 2005;1073(1):383–92.CrossRefGoogle Scholar
  92. 92.
    Qin X, et al. Metabolic fingerprinting by 1HNMR for discrimination of the two species used as Radix Bupleuri. Planta Med. 2012;78(09):926–33.PubMedCrossRefPubMedCentralGoogle Scholar
  93. 93.
    Tistaert C, et al. Dissimilar chromatographic systems to indicate and identify antioxidants from Mallotus species. Talanta. 2011;83(4):1198–208.PubMedCrossRefPubMedCentralGoogle Scholar
  94. 94.
    Tian RT, Xie PS, Liu HP. Evaluation of traditional Chinese herbal medicine: Chaihu (Bupleuri Radix) by both high-performance liquid chromatographic and high-performance thin-layer chromatographic fingerprint and chemometric analysis. J Chromatogr A. 2009;1216(11):2150–5.PubMedCrossRefPubMedCentralGoogle Scholar
  95. 95.
    Qin X, Dai Y, Liu N, et al. Metabolic fingerprinting by 1HNMR for discrimination of the two species used as Radix Bupleuri. Planta Med. 2012;78(09):926–33.PubMedCrossRefPubMedCentralGoogle Scholar
  96. 96.
    Fukuda E, et al. Identification of Glycyrrhiza species by direct analysis in real time mass spectrometry. Nat Prod Commun. 2010;5(11):1755–8.PubMedPubMedCentralGoogle Scholar
  97. 97.
    Pan R, et al. Development of the chromatographic fingerprint of Scutellaria barbata D. Don by GC–MS combined with Chemometrics methods. J Pharm Biomed Anal. 2011;55(3):391–6.PubMedCrossRefPubMedCentralGoogle Scholar
  98. 98.
    Zhao Y, et al. An expeditious HPLC method to distinguish Aconitum kusnezoffii from related species. Fitoterapia. 2009;80(6):333–8.PubMedCrossRefPubMedCentralGoogle Scholar
  99. 99.
    Wu H. Studies on warm and cold nature of JiangHuang and YuJin based on metabonomics. Chinese Academy of Chinese Medical Sciences: Beijing; 2011.Google Scholar
  100. 100.
    Dan M, et al. Metabolite profiling of Panax notoginseng using UPLC–ESI-MS. Phytochemistry. 2008;69(11):2237–44.PubMedCrossRefPubMedCentralGoogle Scholar
  101. 101.
    Grubesic RJ, et al. Spectrophotometric method for polyphenols analysis: Prevalidation and application on Plantago L. species. J Pharm Biomed Anal. 2005;39(3):837–42.PubMedCrossRefPubMedCentralGoogle Scholar
  102. 102.
    Fukuda E, et al. Application to classification of mulberry leaves using multivariate analysis of proton NMR metabolomic data. Nat Prod Commun. 2011;6(11):1621.PubMedPubMedCentralGoogle Scholar
  103. 103.
    Kong W, et al. Quantitative and chemical fingerprint analysis for quality control of Rhizoma Coptidischinensis based on UPLC-PAD combined with chemometrics methods. Phytomedicine. 2009;16(10):950–9.PubMedCrossRefPubMedCentralGoogle Scholar
  104. 104.
    Lai Y, Ni Y, Kokot S. Discrimination of Rhizoma Corydalis from two sources by near-infrared spectroscopy supported by the wavelet transform and least-squares support vector machine methods. Vib Spectrosc. 2011;56(2):154–60.CrossRefGoogle Scholar
  105. 105.
    Zou P, Hong Y, Koh H. Chemical fingerprinting of Isatis indigotica root by RP-HPLC and hierarchical clustering analysis. J Pharm Biomed Anal. 2005;38(3):514–20.CrossRefGoogle Scholar
  106. 106.
    Chen Y, et al. Discrimination of Ganoderma lucidum according to geographical origin with near infrared diffuse reflectance spectroscopy and pattern recognition techniques. Anal Chim Acta. 2008;618(2):121–30.PubMedCrossRefPubMedCentralGoogle Scholar
  107. 107.
    Gong F, et al. Gas chromatography-mass spectrometry and chemometric resolution applied to the determination of essential oils in Cortex cinnamomi. J Chromatogr A. 2001;905(1):193–205.PubMedCrossRefPubMedCentralGoogle Scholar
  108. 108.
    Guo F, et al. Analyzing of the volatile chemical constituents in Artemisia capillaris herba by GC–MS and correlative chemometric resolution methods. J Pharm Biomed Anal. 2004;35(3):469–78.PubMedCrossRefPubMedCentralGoogle Scholar
  109. 109.
    Li W, et al. Classification and quantification analysis of Radix scutellariae from different origins with near infrared diffuse reflection spectroscopy. Vib Spectrosc. 2011;55(1):58–64.CrossRefGoogle Scholar
  110. 110.
    Kim N, et al. Metabolomic approach for age discrimination of Panax ginseng using UPLC-Q-Tof MS. J Agric Food Chem. 2011;59(19):10435–41.PubMedCrossRefPubMedCentralGoogle Scholar
  111. 111.
    Yang SO, et al. NMR-based metabolic profiling and differentiation of ginseng roots according to cultivation ages. J Pharm Biomed Anal. 2012;58(1):19–26.PubMedCrossRefPubMedCentralGoogle Scholar

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© Springer Nature Singapore Pte Ltd.and Shanghai Scientific and Technical Publishers 2019

Authors and Affiliations

  1. 1.National Resource Center for Chinese Materia MedicaChina Academy of Chinese Medical SciencesBeijingChina
  2. 2.Department of Plant BiologyUniversity of California, DavisCaliforniaUSA

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