Applied Biological Chemistry

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

Phytochemical profiles of Brassicaceae vegetables and their multivariate characterization using chemometrics

  • Kyoung Bok Lee
  • Ye Jin Kim
  • Hyo Jin Kim
  • Jaehyuk Choi
  • Jae Kwang Kim


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.


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



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.


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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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