In:
Natural Product Communications, SAGE Publications, Vol. 6, No. 11 ( 2011-11), p. 1934578X1100601-
Abstract:
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 1 H 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.
Type of Medium:
Online Resource
ISSN:
1934-578X
,
1555-9475
DOI:
10.1177/1934578X1100601116
Language:
English
Publisher:
SAGE Publications
Publication Date:
2011
detail.hit.zdb_id:
2430442-6
SSG:
15,3
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