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, 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.
Department of Natural Medicine and Phytochemistry, Meiji Pharmaceutical University, 2-522-1 Noshio, Kiyose, Tokyo 204-8588, Japan.
Eriko
E
Fukuda
Motoyuki
M
Yoshida
Masaki
M
Baba
Yoshihiro
Y
Uesawa
Ryuichiro
R
Suzuki
Osamu
O
Kamo
Koji
K
Tsubono
Kazunori
K
Arifuku
Kazuhisa
K
Yatsunami
Yoshihito
Y
Okada
D009682:Magnetic Resonance Spectroscopy; D055442:Metabolome; D055432:Metabolomics; D031625:Morus / Q000737:chemistry* / Q000145:classification / Q000378:metabolism; D015999:Multivariate Analysis; D010936:Plant Extracts / Q000737:chemistry / Q000145:classification; D018515:Plant Leaves / Q000737:chemistry / Q000145:classification / Q000378:metabolism; D025341:Principal Component Analysis
D010936:Plant Extracts