The total free amino acid of green tea was nondestructive estimated by near-infrared (NIR) spectroscopy combined with multivariate calibration, compared the performance of back propagation neural networks (BP-NN) and partial least squares (PLS) regression analysis. The original spectra of tea samples in wavelength range of 10000-4000cm− 1 were acquired. Spectral pretreatment methods were applied to reduce the systematic noise, and enhance the contribution of the chemical composition. The model was optimized by cross validation, and its performance was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. Experimental results showed that the performance of BP-NN model was superior to the performances of PLS model, from the point of view of the predictive ability. The optimal results of the BP-NN model with multiplicative scatter correction spectral pretreatment were achieved as follow: RMSEP=0.246 and Rp=0.958 in the prediction set, respectively. It can be concluded that NIR spectroscopy combined with BP-NN has significant potential in quantitative analysis and monitor of free amino acid content in green tea.