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Complete sets of 1H chemical shifts and spin coupling constants of testosterone and 17α‐methyltestosterone were determined by analysis of the 600 MHz 1H NMR spectra. The spin coupling constants of the two compounds have similar values, but the substitution of a methyl group at the 17α‐position changes the 1H chemical shifts of some protons on the C and D rings: low‐field shifts for H‐12α, H‐14α and H‐16α and high‐field shifts for H‐12β and H‐16β. The two‐dimensional 1H‐1H COSY and NOESY methods were used for unambiguous assignments, including those for the signals of H‐16α and H‐16β which are interchanged in the present paper from the assignment reported previously. Two‐dimensional 1H‐13C shift correlated spectroscopy was also used to complement the 1H and 13C assignments. The effect on the 13C chemical shifts of methyl substitution at the 17α‐position is briefly discussed.

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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.

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