Abstract：
In this study, near-infrared (NIR) spectroscopy and high-performance liquid chromatography (HPLC) combined with chemometrics tools were applied for quick discrimination and quantitative analysis of different varieties and origins of Atractylodis rhizoma samples. Based on NIR data, orthogonal partial least squares discriminant analysis (OPLS-DA) and K-nearest neighbor (KNN) models achieved greater than 90% discriminant accuracy of the three species and two origins of Atractylodis rhizoma. Moreover, the contents of three active ingredients (atractyloxin, atractylone, and β-eudesmol) in Atractylodis rhizoma were simultaneously determined by HPLC. There are significant differences in the content of the three components in the samples of Atractylodis rhizoma from different varieties and origins. Then, partial least squares regression (PLSR) models for the prediction of atractyloxin, atractylone, and β-eudesmol content were successfully established. The complete Atractylodis rhizoma spectra gave rise to good predictions of atractyloxin, atractylone, and β-eudesmol content with R2 values of 0.9642, 0.9588, and 0.9812, respectively. Based on the results of this present research, it can be concluded that NIR is a great nondestructive alternative to be applied as a rapid classification system by the drug industry.
摘要：
在这项研究中,应用近红外(NIR)光谱和高效液相色谱(HPLC)结合化学计量学工具对不同品种和产地的苍术样品进行快速鉴别和定量分析。根据NIR数据，正交偏最小二乘判别分析（OPLS-DA）和K最近邻（KNN）模型对苍术的三个物种和两个起源的判别准确率均大于90％。此外，三种活性成分的含量(白术，atractylone,采用高效液相色谱法同时测定苍术中β-eudesmol）的含量。不同品种和产地的苍术样品中三种成分的含量存在显著差异。然后，偏最小二乘回归(PLSR)模型预测白术苷，atractylone,并成功建立了β-eudesmol含量。完整的苍术光谱产生了白术素的良好预测，atractylone,和β-eudesmol含量，R2值分别为0.9642、0.9588和0.9812。根据本研究的结果，可以得出结论，NIR是一种很好的非破坏性替代方法，可用作制药行业的快速分类系统。
