中药大黄的鉴别研究

【作者】 汤彦丰；

【导师】 张卓勇；

【作者基本信息】 首都师范大学 ， 分析化学， 2004， 硕士

【摘要】 大黄为蓼科大黄属植物，具有重要的药用价值和经济价值。中国药典收载的正品大黄为掌叶大黄、唐古特大黄和药用大黄的根及根茎。大黄属的其它种类在不同地区及民间也作药用。在商品中常常混有非正品大黄的根和根茎，但其泻下作用不及正品大黄，有些还可能引起腹痛。为了确保大黄及其产品的临床疗效，需要对大黄样本进行鉴定。长期以来，对大黄生药的鉴定多是依靠其外部形态、性状鉴定、显微鉴定和理化鉴定，这些方法在一定程度上依赖于经验，且难以区别正品大黄和非正品大黄的根及炮制加工后的粉末。在本文中，使用Foss近红外光谱仪和Perkin Elmer公司的傅里叶中红外光谱仪对52种不同产地的大黄样品进行图谱扫描。正品大黄和非正品大黄的近红外光谱彼此之间非常相似，而它们的中红外光谱彼此也相似，很难用一般方法鉴别。采用反向传播多层前向神经网络(BP-ANN)、高木—关野模糊系统(T-S)对大黄的近红外图谱和中红外图谱进行分类判别，鉴别正品大黄和非正品大黄。人工神经网络具有容错能力，在解决多输入非线性及复杂问题时具有很大的优越性。模糊逻辑是一种精确解决不精确不完全信息的方法。模糊逻辑系统是指那些与模糊概念和模糊逻辑有直接关系的系统。 本工作的研究结果表明：这些方法都可以作为鉴别中草药大黄的新方法。对大黄的近红外图谱鉴别中，BP-ANN的识别率为96％，高木—关野模糊系统(T-S)的识别率为100％。对大黄的中红外图谱鉴别中，BP-ANN的识别率为98％，高木—关野模糊系统(T-S)的识别率为100％。总之，这些结果表明高木—关野模糊系统(T-S)优于BP-ANN。更多还原

【Abstract】 Rhubarb is an important traditional medicinal plant. In Chinese pharmacopoeia, however, only three species are assigned as official rhubarbs: R. palmatem, R. tanguticum, and R. officeinale. Root and rootstalk of unofficial rhubarb were often mixed into official rhubarb in commercial products. But the medicinal function of unofficial rhubarb is not as good as the official rhubarb. In order to ensure the safety and curative effect of rhubarb in clinic practice, the discrimination of the official rhubarb is usually based on the shape, exterior configuration, microscopic features, and chemical components. But these methods can not be used to identify the powdered official and unofficial rhubarb samples.In this paper, back-propagation neural network (BP-ANN) and Takagi-Sugeno fuzzy system were applied to identify official rhubarb and unofficial rhubarb samples based on NIR spectra and mid-IR spectra. Neural networks are suitable for processing large amount of complicated data. The neural networks have some advantages over many other methods. Fuzzy logic is a mathematical method for solving inexact problems accurately. The T-S fuzzy system used in this paper is such a kind of fuzzy network. NIR spectra and mid-IR spectra were collected by using NIR spectrometers and mid-IR spectrometers (FOSS NIRSystem and Perkin Elmer, respectively).Results showed that these methods can be used to discriminate Chinese herbal medicine. The correctness of BP-ANN is 96% and the correctness of T-S is 100% by using NIR spectra of rhubarb. The correctness of BP-ANN is 98% and the correctness of T-S is 100% by using mid-IR spectra of rhubarb. In summary, the results obtained by Takagi-Sugeno fuzzy system are better than those by BP-ANN.更多还原