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出 处:《药物分析杂志》2017年第2期345-351,共7页Chinese Journal of Pharmaceutical Analysis
基 金:重庆市科委自然科学基金计划资助项目(CSTC2006BB5303)
摘 要:目的:建立同时快速鉴别3种药典收载麻黄药材的近红外光谱法。方法:测量药典品种麻黄药材草麻黄、中麻黄和木贼麻黄,以及混淆品种丽江麻黄的傅里叶变换近红外漫反射光谱,采用化学计量学技术,提取不同药典品种麻黄药材共有的且区别于混淆品种丽江麻黄的光谱特征信息,建立同时鉴别不同药典品种麻黄药材与混淆品种丽江麻黄的判别分析(discriminant analysis,DA)模型和对向传播人工神经网络(counterpropagation artificial neural network,CP-ANN)模型,并比较2种模型的性能。结果:所建DA模型校正集和验证集的预测准确率均为100.0%;CP-ANN模型校正集、交叉验证和验证集的预测准确率均为100.0%。虽然2种模型具有相同的预测准确率,但是非线性CP-ANN模型使用较少的主成分建模,性能略优于线性DA模型。结论:所建近红外光谱法能够同时快速鉴别3种药典收载麻黄药材。Objective:To identify three species of Ephedra authorized in pharmacopoeia simultaneously and rapidly using near-infrared(NIR)spectroscopy.Methods:The Fourier transform NIR diffuse reflectance spectra of the Ephedra authorized in pharmacopoeia(Ephedra sinica Stapf,Ephedra intermedia Schrenk et C.A.Mey.and Ephedra equisetina Bge.)and those of the adulterant species(Ephedra likiangensis Florin)were collected.The common spectral features,which differed from adulterant species,were extracted from different species of Ephedra authorized in pharmacopoeia using chemometric techniques.The discriminant analysis(DA)model and the counterpropagation artificial neural network(CP-ANN)model were built for identifying the different species of Ephedra authorized in pharmacopoeia from Ephedra likiangensis Florin.The performances of two models were compared.Results:The prediction accuracy of the DA model was 100.0% for both calibration and validation.And the prediction accuracy of the CP-ANN model was 100.0% for all of calibration,cross-validation and validation.Although the two models had the same prediction accuracy,the nonlinear CP-ANN model was slightly better than the linear DA model since it used less principal components(PCs).Conclusion:The three species of Ephedra authorized in pharmacopoeia could be simultaneously and rapidly identified by using the established NIR spectroscopy method.
关 键 词:药典麻黄 草麻黄 中麻黄 木贼麻黄 丽江麻黄 鉴别 傅里叶变换近红外漫反射光谱法
分 类 号:R917[医药卫生—药物分析学]
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