Applied Biological Chemistry

, Volume 61, Issue 2, pp 131–144 | Cite as

Phytochemical profiles of Brassicaceae vegetables and their multivariate characterization using chemometrics

Article
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Abstract

Twenty-eight metabolites were extracted from nine Brassicaceae of Korean origin (broccoli, Brussels sprouts, cabbage, Chinese cabbage, kale, kohlrabi, pak choi, radish sprouts, and red cabbage) and analyzed using gas chromatography–mass spectrometry and high-performance liquid chromatography. Principal components analysis (PCA), orthogonal projection to latent structure-discriminant analysis (OPLS-DA), Pearson’s correlation analysis, hierarchical clustering analysis (HCA), and batch learning self-organizing map analysis (BL-SOM) were used to visualize metabolite pattern differences among Brassicaceae samples. The PCA score plots from the metabolic data sets provided a clear distinction between Brassica species and radish sprouts (genus Raphanus L.). Additionally, B. oleracea L. varieties were differentiated from B. rapa L. varieties by PCA and OPLS-DA score plots. HCA and BL-SOM of these metabolites clustered metabolites that are metabolically related. This study demonstrates that plants’ characterization by multivariate statistical analysis using metabolic profiling allows distinguishing their phenotypes and identifying desired characteristics.

Keywords

Batch learning self-organizing map analysis Bioinformatics Metabolic profiling Metabolomics Principal components analysis 

Notes

Acknowledgments

This work was supported by a grant from the Incheon National University Research Grant in 2014, Republic of Korea. The authors are grateful to Dr. Shigehiko Kanaya for the kind gift of a BL-SOM program.

References

  1. 1.
    Salunkhe DK, Kadam SS (1998) Handbook of vegetable science and technology: production, composition. Storage and Processing. Marcel Dekker Inc, New York, pp 533–538Google Scholar
  2. 2.
    Howard LA, Jeffery EH, Wallig MA, Klein BP (1997) Retention of phytochemicals in fresh and processed broccoli. J Food Sci 62:1098–1100CrossRefGoogle Scholar
  3. 3.
    Femina A, Robertson JA, Waldron KW (1998) Cauliflower (Brassica oleracea L.), globe artichoke (Cynara scolymus L.) and chicory witloof (Cichorium intybus L.) processing by-products as source of dietary fibre. J Sci Food Agric 77:511–518CrossRefGoogle Scholar
  4. 4.
    Kumar S, Andy A (2012) Health promoting bioactive phytochemicals from Brassica. Int Food Res J 19:141–152Google Scholar
  5. 5.
    Park WT, Kim JK, Park S, Lee SW, Li X, Kim YB, Uddin MR, Park NI, Kim SJ, Park SU (2012) Metabolic profiling of glucosinolates, anthocyanins, carotenoids, and other secondary metabolites in kohlrabi (Brassica oleracea var. gongylodes). J Agric Food Chem 60:8111–8116CrossRefGoogle Scholar
  6. 6.
    Park SY, Lim SH, Ha SH, Yeo YS, Park WT, Kwon DY, Park SU, Kim JK (2013) Metabolite profiling approach reveals the interface of primary and secondary metabolism in colored cauliflowers (Brassica oleracea L. ssp. botrytis). J Agric Food Chem 61:6999–7007CrossRefGoogle Scholar
  7. 7.
    Iniguez-Luy FL, Voort AV, Osborn TC (2008) Development of a set of public SSR markers derived from genomic sequence of a rapid cycling Brassica oleracea L. genotype. Theor Appl Genet 117:977–985CrossRefGoogle Scholar
  8. 8.
    Kim TJ, Lee KB, Baek SA, Choi JH, Ha SH, Lim SH, Park SY, Yeo YS, Park SU, Kim JK (2015) Determination of lipophilic metabolites for species discrimination and quality assessment of nine leafy vegetables. J Korean Soc Appl Biol Chem 58:909–918CrossRefGoogle Scholar
  9. 9.
    Kim JK, Cho MR, Baek HJ, Ryu TH, Yu CY, Kim MJ, Fukusaki E, Kobayashi A (2007) Analysis of metabolite profile data using batch-learning self-organizing maps. J Plant Biol 50:517–521CrossRefGoogle Scholar
  10. 10.
    Kim JK, Lee SY, Chu SM, Lim SH, Suh SC, Lee YT, Cho HS, Ha SH (2010) Variation and correlation analysis of flavonoids and carotenoids in Korean pigmented rice (Oryza sativa L.) cultivars. J Agric Food Chem 58:12804–12809CrossRefGoogle Scholar
  11. 11.
    Baek SA, Jung YH, Lim SH, Park SU, Kim JK (2016) Metabolic profiling in Chinese cabbage (Brassica rapa L. subsp. pekinensis) cultivars reveals that glucosinolate content is correlated with carotenoid content. J Agric Food Chem 64:4426–4434CrossRefGoogle Scholar
  12. 12.
    Fonville JM, Richards SE, Barton RH, Boulange CL, Ebbels TMD, Nicholson JK, Holmes E, Durnas ME (2010) The evolution of partial least squares models and related chemometric approaches in metabonomics and metabolic phenotyping. J Chemom 24:636–649CrossRefGoogle Scholar
  13. 13.
    Kim YB, Park SY, Park CH, Park WT, Kim SJ, Ha SH, Valan Arasu M, Al-Dhabi NA, Kim JK, Park SU (2016) Metabolomics of differently colored Gladiolus cultivars. Appl Biol Chem 59:597–607CrossRefGoogle Scholar
  14. 14.
    Kim JK, Park SY, Lim SH, Yeo Y, Cho HS, Ha SH (2013) Comparative metabolic profiling of pigmented rice (Oryza sativa L.) cultivars reveals primary metabolites are correlated with secondary metabolites. J Cereal Sci 57:14–20CrossRefGoogle Scholar
  15. 15.
    Hirai MY, Yano M, Goodenowe DB, Kanaya S, Kimura T, Awazuhara M, Arita M, Fujiwara T, Saito K (2004) Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proc Natl Acad Sci USA 101:10205–10210CrossRefGoogle Scholar
  16. 16.
    Park SY, Choi SR, Lim SH, Yeo YS, Kweon SJ, Bae YS, Kim KW, Im KH, Ahn SK, Ha SH, Park SU, Kim JK (2014) Identification and quantification of carotenoids in paprika fruits and cabbage, kale, and lettuce leaves. J Korean Soc Appl Biol Chem 57:355–358CrossRefGoogle Scholar
  17. 17.
    Messerli G, Partovi Nia V, Trevisan M, Kolbe A, Schauer N, Geigenberger P, Chen J, Davison AC, Fernie AR, Zeeman SC (2007) Rapid classification of phenotypic mutants of Arabidopsis via metabolite fingerprinting. Plant Physiol 143:1484–1492CrossRefGoogle Scholar
  18. 18.
    Pongsuwan W, Fukusaki E, Bamba T, Yonetani T, Yamahara T, Kobayashi A (2007) Prediction of Japanese green tea ranking by gas chromatography/mass spectrometry-based hydrophilic metabolite fingerprinting. J Agric Food Chem 55:231–236CrossRefGoogle Scholar
  19. 19.
    Jumtee K, Bamba T, Fukusaki E (2009) Fast GC-FID based metabolic fingerprinting of Japanese green tea leaf for its quality ranking prediction. J Separation Sci 32:2296–2304CrossRefGoogle Scholar
  20. 20.
    Eriksson L, Johansson E, Kettaneh-Wold N, Wold S (2001) Multi- and megavariate data analysis principles and applications. Umetrics AB, UmeåGoogle Scholar
  21. 21.
    Disch A, Hemmerlin A, Bach TJ, Rohmer M (1998) Mevalonate-derived isopentenyl diphosphate is the biosynthetic precursor of ubiquinone prenyl side chain in tobacco BY-2 cells. Biochem J 331:615–621CrossRefGoogle Scholar
  22. 22.
    Laule O, Furholz A, Chang HS, Zhu T, Wang X, Heifetz PB, Gruissem W, Lange BM (2003) Crosstalk between cytosolic and plastidial pathways of isoprenoid biosynthesis in Arabidopsis thaliana. Proc Natl Acad Sci USA 100:6866–6871CrossRefGoogle Scholar
  23. 23.
    Arigoni D, Sagner S, Latzel C, Eisenreich W, Bacher A, Zenk MH (1997) Terpenoid biosynthesis from 1-deoxy-d-xylulose in higher plants by intramolecular skeletal rearrangement. Proc Natl Acad Sci USA 94:10600–10605CrossRefGoogle Scholar
  24. 24.
    Kanaya S, Kinouchi M, Abe T, Kudo Y, Yamada Y, Nishi T, Mori H, Ikemura T (2001) Analysis of codon usage diversity of bacterial genes with a self-organizing map (SOM): characterization of horizontally transferred genes with emphasis on the E. coli O157 genome. Gene 276:89–99CrossRefGoogle Scholar
  25. 25.
    Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43:59–69CrossRefGoogle Scholar
  26. 26.
    Kohonen T (1990) The self-organizing map. Proc IEEE 78:1464–1480CrossRefGoogle Scholar
  27. 27.
    Kohonen T, Oja E, Simula O, Visa A, Kangas J (1996) Engineering applications of the self-organizing map. Proc IEEE 84:1358–1384CrossRefGoogle Scholar
  28. 28.
    Young AJ (1991) The photoprotective role of carotenoids in higher plants. Physiol Plant 83:702–708CrossRefGoogle Scholar
  29. 29.
    Goodacre R, Roberts L, Ellis DI, Thorogood D, Reader SM, Ougham H, King I (2007) From phenotype to genotype: whole tissue profiling for plant breeding. Metabolomics 3:489–501CrossRefGoogle Scholar

Copyright information

© The Korean Society for Applied Biological Chemistry 2018

Authors and Affiliations

  1. 1.Division of Life Sciences and Bio-Resource and Environmental CenterIncheon National UniversityIncheonRepublic of Korea

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