In order to identify and predict major components for white tea,oolong tea and black tea in the same time,the 3 different tea products were collected,and the qualitative and quantitative models were established based on NIRs (near infrared spectroscopy).The qualitative classification model,which was established by factor-method,could identify full of the independent validation set samples.At the same time,partial least squares (PLS) algorithm and principal component analysis (PCA) were conducted on the calibration of regression model.The performance of the final model was evaluated according to root mean square error of cross-validation (RMSECV),root mean square error of prediction (RMSEP) and correlation coefficient (R).The correlations coefficients (R) in the prediction set were achieved as follows:0.991 3 for moisture model,0.905 7 for tea polyphenols model,and 0.974 3 for caffeine model.It was demonstrated that it was feasible to establish a “saving” qualitative and quantitative prediction model for 3 different kinds of tea products with the premise of accuracy.