Niu Zhi-you, Lin Xin
College of Engineering & Technology, Huazhong Agricultural University, Wuhan 430070, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2009 Sep;29(9):2417-20.
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 (Rp2) 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.
从中国不同地区采集了四种茶叶，包括茉莉花茶、苦丁茶、龙井茶和铁观音。共制备了120个样品（每个品种30个样品）。使用NIRSystem6500分析仪获得原始样品光谱。采用近红外光谱技术对茶叶进行定性和定量分析。运用主成分分析和判别分析来区分这四种茶叶。根据不同光谱数据预处理方法和不确定因素系数的比较，建立了定性鉴别的最佳校准模型。采用改进的偏最小二乘法建立了茶叶中水分、茶多酚和咖啡因的定量分析模型。结果表明，验证集中四种茶叶的准确识别率达到100%。独立验证集的决定系数（Rp2）和相对预测偏差（RPD）分别大于0.91和3.0。得出结论，近红外光谱法可作为快速检测茶叶品种和化学成分 的方法。
