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目的对不同品种产地麻黄进行快速鉴别,为其正确使用提供科学依据。方法采用傅里叶变换红外光谱(FTIR)测定了6个不同品种产地的36个麻黄样品的红外光谱。选择1 000~1 400 cm-1范围内的光谱数据进行了主成分聚类分析和径向基神经网络预测。结果主成分分析(PCA)表明,前3个主成分的累积可信度已达96.44%,能够表征出麻黄在不同品种产地的多样性分化。建立概率径向基神经网络模型对12个麻黄样本进行了预测,正确率达83.33%。结论傅里叶变换红外光谱技术结合化学统计学方法可用于麻黄药材品种产地的分类和鉴别,可为麻黄药材的质量控制提供一个快捷、准确、可行的方法。
Objective Rapid identification of ephedra from different areas of origin, to provide a scientific basis for its correct use. Methods Fourier transform infrared spectroscopy (FTIR) was used to determine the infrared spectra of 36 ephedra samples from six different producing areas. The principal components clustering analysis and radial basis function neural network prediction were performed using the spectral data in the range of 1 000 ~ 1 400 cm-1. Results The principal component analysis (PCA) showed that the cumulative confidence of the first three principal components reached 96.44%, which could characterize the diversity of ephedra in different producing areas. Probabilistic radial basis neural network model was established to predict 12 ephedra samples, with a correct rate of 83.33%. Conclusion Fourier transform infrared spectroscopy combined with chemical statistical methods can be used for classification and identification of ephedra medicinal herbs origin, which can provide a quick, accurate and feasible method for the quality control of ephedra herb.