分别采集了茉莉花茶、 苦丁茶、 龙井和铁观音4个种类茶叶共120个样本， 利用NIRSystems6500型近红外光谱分析仪对样本进行光谱测量， 应用近红外光谱分析技术对茶叶进行定性和定量分析。 采用主成分分析法， 结合聚类分析法， 对4种类别的茶叶进行定性鉴别， 通过对不同光谱数据预处理方式和不确定因子系数进行比较， 确立了最优定性判别定标模型。 同时， 采用修正的偏最小二乘法， 比较不同光谱预处理方法对定标模型的影响， 建立了茶叶中水分、 茶多酚和咖啡碱含量的定量分析模型， 并对未知样本进行预测。 定性分析模型的种类识别准确率达到100％， 定量分析模型的决定系数均大于0.91， 相对分析误差RPD均大于3。 结果表明， 利用NIRS分析技术可以快速定性和定量分析鉴别茶叶的类别和成分含量。

Four varieties of tea were collected from different areas in China including jasmine tea, Kuding tea, Longjing tea and Tieguanyin. A total of 120 samples (30 samples for each variety) were prepared. The original samples spectra were obtained using NIRSystem6500 analyzer. Tea was analyzed qualitatively and quantitatively by near infrared spectroscopy technology. Principal component analysis and discriminant analysis were used to distinguish the four varieties of tea. The optimal calibration model for qualitative discrimination was established according to comparison of different spectral data pretreatment methods and the uncertain factor coefficients. Quantitative analysis models for moisture content, tea polyphenol and caffeine in tea were developed with modified partial least square. The results show that the accurate recognition rate for the four varieties of tea in the validation set reached 100%. The coefficients of determination (Ｒ２ｐ) and relative prediction deviation (RPD) of independent validation sets were more than 0.91 and 3.0, respectively. It is concluded that NIRS can be used as a rapid method to detect the variety and chemical components in tea.