Application to classification of mulberry leaves using multivariate analysis of proton NMR metabolomic data.

摘要: 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 was able be applied natural products and/or material field. Each extraction of 24 leaf samples, divided into six locations from tip stem in each four strains, analyzed by pattern recognition methods, known as Principal Component Analysis (PCA) and Soft Independent Modeling Class Analogy (SIMCA). Twenty-four extracts mulberry showed independent spectra 1H NMR. The separation data due difference at achieved PCA score plot correlation PC1 (86.1%) PC3 (4.6%) two loading plots, suggesting classification position an variable plot. Moreover, clarified seven highest discrimination powers SIMCA method. Meanwhile, obtained variety strains with three but method did not give a peak classification.