Application to Classification of Mulberry Leaves using Multivariate Analysis of Proton NMR Metabolomic Data

Recently, NMR-based metabolomic analysis has been used to acquire information based on differentiation among biological samples. In the present study, we examined whether multivariate analysis was able to be applied to natural products and/or material field. Each extraction of 24 leaf samples, divid...

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Published in:Natural product communications 2011-11, Vol.6 (11), p.1621-1625
Main Authors: Fukuda, Eriko, Yoshida, Motoyuki, Baba, Masaki, Uesawa, Yoshihiro, Suzuki, Ryuichiro, Kamo, Osamu, Tsubono, Koji, Arifuku, Kazunori, Yatsunami, Kazuhisa, Okada, Yoshihito
Format: Article
Language:English
Subjects:
Magnetic Resonance Spectroscopy
Metabolome
Metabolomics
Morus - chemistry
Morus - classification
Morus - metabolism
Multivariate Analysis
Plant Extracts - chemistry
Plant Extracts - classification
Plant Leaves - chemistry
Plant Leaves - classification
Plant Leaves - metabolism
Principal Component Analysis
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Summary:Recently, NMR-based metabolomic analysis has been used to acquire information based on differentiation among biological samples. In the present study, we examined whether multivariate analysis was able to be applied to natural products and/or material field. Each extraction of 24 leaf samples, divided into six locations from the tip of the stem in each of four strains, was analyzed by pattern recognition methods, known as Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA). Twenty-four extracts from mulberry leaf showed independent spectra by 1H NMR. The separation of leaf extraction data due to the difference at six locations was achieved in the PCA score plot as correlation PC1 (86.1%) and PC3 (4.6%) and showed two loading plots, suggesting classification by leaf position as an independent variable in the loading plot. Moreover, the difference among six locations clarified the seven highest discrimination powers by the SIMCA method. Meanwhile, the PCA score plot obtained classification by the variety of mulberry strains with three loading plots, but the SIMCA method did not give a peak by classification. Our findings demonstrate that NMR multivariate analysis was able to be applied to the classification of mulberry leaf extracts by leaf position and strain.
ISSN:1934-578X
1555-9475
DOI:10.1177/1934578X1100601116