液-质联用法测定铁皮石斛和西洋参及制剂中多菌灵残留

用高效液相色谱-质谱联用法定量检测铁皮石斛、西洋参药材及其制剂中的多菌灵残留。在酸性条件下以甲醇提取中药材和制剂中的残留多菌灵,分析色谱柱为D iamonsil C18(4.6 mm i.d.×250 mm,5μm),以4%四氢呋喃水溶液-甲醇梯度洗脱,采用选择离子监测模式,以m/z192为检测定量离子。多菌灵出峰时间在27 m in左右,线性范围为0.22~33 ng,相关系数0.9993;方法检出限为0.015 mg/L;定量限为0.022mg/L;平均回收率在95.6%以上。该方法灵敏、准确、可靠,定量范围宽,耐用性强,可作为中药中多菌灵残留的可靠检测方法。     

西洋参中无机元素的主成分分析和聚类分析

采用电感耦合等离子体质谱(ICP/MS)法测定了12个西洋参样品中18种无机元素的含量,建立西洋参无机元素指纹谱,并用SPSS主成分分析法对西洋参中的特征元素进行分析;主成分分析选出五个主因子,得出西洋参的特征元素为Fe,Al,V,Mn,Mg,Sr,Mo,Ca,Cu;聚类分析将12个西洋参样品聚成五大类,表明元素的分布特征与西洋参的品种关系显著;主成分分析法和聚类分析法是西洋参无机元素分析的有效方法.     

基于FTIR-SVM的西洋参与籽播参的分类研究

支持向量机(SVM)是根据统计理论提出的一种新的学习算法。文章以40个西洋参样品为实验材料,通过FTIR-SVM建立了西洋参样品与籽播参识别的模型。对学习训练集中的30个样品模型识别率为100%,对10个预测样品的识别准确率为90%。研究结果表明,FTIR-SVM可以用于中药西洋参与籽播参的区别。     

Isolation and Characterization of a Bioactive Polysaccharide from Panax Quinquefolium L.

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20(R)和20(S)-人参皂甙Rg_2碳氢NMR信号全指定

20 (R)和 2 0 (S) 人参皂甙 Rg2 属于达玛烷型四环三萜类化合物 .应用 2DNMR技术 :1 H 1 HCOSY、HMQC和HMBC全归属 2 0 (R)和 2 0 (S) 人参皂甙 Rg2 碳和氢质子信号 ,为该类型化合物的结构鉴定提供波谱学依据 .     

中药材三七提取液近红外光谱的支持向量机回归校正方法

提出近红外光谱的支持向量机回归校正建模方法.以中药材三七渗漉提取液为实际分析对象,对其近红外光谱数据进行预处理和主成分分析后,用支持向量机回归算法建立人参皂苷R_g1,R_b1和R_d以及三七总皂苷的近红外光谱校正模型.以R_g1,R_b1和R_d的HPLC测定值及三七总皂苷的比色法测定值为参照,将本文方法与偏最小二乘回归和径向基神经网络建模方法相比较,结果表明,本文所建模型的预测准确性优于后两者,可推广应用于中药提取过程的近红外光谱分析.     

人参中皂苷类化合物的HPLC-ESI MS研究

利用高效液相色谱与电喷雾质谱联用技术分析鉴定了人参粗提物中人参皂苷类化合物。实验采用反相C18色谱柱,二元线性梯度洗脱（0．2％的醋酸水溶液和乙腈）,分离并检测了人参中的皂苷类化合物：通过与电喷雾质谱的联用和质谱的源内CID技术获得了其中主要色谱峰相对应化合物的相对分子质量和结构信息。     

A new compound with cytotoxic activities from the leaves of Panax ginseng C.A. Meyer

A new compound, 3,6,20（S）-trihydroxy- 12,23-epoxydammar-24-ene,6,20-di-O-β-D-glucopyranoside （1）, was isolated from the leaves of Panax ginseng C.A. Meyer, whose structural elucidation was carried out by means of spectral analysis （including IR, HR- FAB-MS and NMR）. This compound showed the moderate cytotoxic activities against U937 and HeLa cells by using the MTT method.     

人参茎叶中一个新天然产物的结构研究

采用多种色谱方法从人参茎叶中分离得到一个新天然产物-达玛烷型三萜皂苷元dammar 20(22), 24-diene-3β,6α,12β-triol,并利用质谱、核磁共振谱和化学方法对此化合物进行了结构解析. 通过2D NMR（HMQC、HMBC）进行了¹H和¹³C NMR信号全归属,并纠正了文献中的个别化学位移的信号指认错误.     

Determination of Active Components in a Natural Herb with Near Infrared Spectroscopy Based on Artificial Neural Networks

The non-linear relationships between the contents of ginsenoside Rg1, Rb2, Rd and Panax notoginseng saponins(PNS) in Panax notoginseng root herb and the near infrared(NIR) diffuse reflectance spectra of the herb were established by means of artificial neural networks(ANNs). Four three-layered perception feed-for-ward networks were trained with an error back-propagation algorithm. The significant principal components of the NIR spectral data matrix were utilized as the input of the networks. The networks architecture and parameters were selected so as to offer less prediction errors. Relative prediction errors for Rg1, Rb1, Rd and PNS obtained with the optimum ANN models were 8.99%, 6.54%, 8.29%, and 5.17%, respectively, which were superior to those obtained with PLSR methods. It is verified that ANN is a suitable approach to model this complex non-linearity. The developed method is fast, non-destructive and accurate and it provides a new efficient approach for determining the active components in the complex system of natural herbs.