State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China.
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Sep 5;258:119798. doi: 10.1016/j.saa.2021.119798. Epub 2021 Apr 9.
Geographical origin is an important factor affecting the quality of traditional Chinese medicine. In this paper, the identification of geographical origin of Gastrodia elata was performed by using excitation-emission matrix fluorescence and chemometric methods. Firstly, excitation-emission matrix (EEM) fluorescence spectra of Gastrodia elata samples from different geographical origins were obtained. And then three chemometric methods, including multilinear partial least squares discriminant analysis (N-PLS-DA), unfold partial least squares discriminant analysis (U-PLS-DA), and k-nearest neighbor (kNN) method, were applied to build discriminant models. Finally, 45 Gastrodia elata samples could be differentiated from each other by these classification models according to their geographical origins. The results showed that all models obtained good classification results. Compared with the N-PLS-DA and U-PLS-DA, kNN got more accurate and reliable classification results and could identify Gastrodia elata samples from different geographical origins with 100% accuracy on the training and test set. Therefore, the proposed method was available for easily and quickly distinguishing the geographical origin of Gastrodia elata, which can be considered as a promising alternative method for determining the geographic origin of other traditional Chinese medicines.
地理来源是影响中药质量的重要因素。本研究采用激发-发射矩阵荧光和化学计量学方法鉴定天麻的地理来源。首先，获得了来自不同地理来源的天麻样品的激发-发射矩阵（EEM）荧光光谱。然后，应用三种化学计量学方法，包括多元线性偏最小二乘判别分析（N-PLS-DA）、 unfold 偏最小二乘判别分析（U-PLS-DA）和 k 最近邻（kNN）法，构建判别模型。最后，根据地理来源，这些分类模型可以将 45 个天麻样品彼此区分开来。结果表明，所有模型均获得了良好的分类结果。与 N-PLS-DA 和 U-PLS-DA 相比，kNN 获得了更准确和可靠的分类结果，并且可以在训练集和测试集上以 100%的准确率识别来自不同地理来源的天麻样品。因此，该方法可用于快速准确地鉴别天麻的地理来源，可作为确定其他中药地理来源的有前途的替代方法。
