In this paper,a nondestructive method was developed for the identification of different varieties of green tea by near infrared spectroscopy with chemometrics.The near infrared spectra of eight varieties of green tea samples were collected.Effects of single and optimized combination preprocessing methods on spectra were compared.The identification models were constructed by unsupervised principal component analysis(PCA) and supervised linear discriminant analysis(LDA).Results showed that the optimized combination preprocessing method achieved higher accuracy.The standard normal variable transformation preprocessing method was used to eliminate the spectral scattering effect caused by the uneven size of tea samples.Meanwhile,the first derivative preprocessing was used to eliminate the changing background,reduce the interference of baseline drift and underline the useful information from spectra.By combining the pretreatment methods with the principal component analysis,the tea samples could be identified more accurately,with an accuracy of 75.00%.In addition,a supervised linear discriminant analysis method was used to process the original spectral data,with an identification accuracy reaching up to 100%.However,a priori knowledge of categories was needed in the method.Therefore,the combination of near infrared spectroscopy and chemometrics could realize the rapid and non destructive identification of different green tea varieties.