Near infrared reflectance spectroscopy was applied to determine the content of protein, starch and amylase in the mungbean grown in China. One hundred and two samples which both in flour and intact seeds were scanned in NIR Systems (MPA, Bruker, Germany) with Infra soft software OPUS 6.5 version. The partial least-squares regression method was developed with cross validation after processing data. The best calibration statistics were obtained by optimization. In flour the largest R2 value and the lowest SECV were for protein (0.95 and 0.329) for starch (0.90 and 0.576) and for amylase (0.89 and 0.307) and the residual prediction deviation (RPD) value were from 3.08 to 4.61, respectively. In intact seeds largest R2 value and the lowest SECV were for protein (0.90 and 0.404), for starch (0.88 and 0.643) and for amylose (0.85 and 0.426) and RPD value were from 2.51 to 3.23, respectively. The robustness of the model was evaluated for external validation. The mean differences, which were found 1.0% to 1.8% in flour, were slightly lower than the differences in intact seeds. NIRS prediction of the three compositions in the flour was applicable as a rapid and simple method. For intact seeds this method was non-destructive and could preserve its vigorous vitality and could be used in seeds conservation and germplasm resources innovation. But further work need to be done to improve the accuracy in the intact seeds.