基于化学识别模式,建立HPLC指纹图谱及多成分含量测定,为金花三宝口服液的质量评价提供参考。采用HPLC法,以Capcell Pak C_(18)(250mm×4.6mm,5μm)为色谱柱,流速为0.8mL·min^(-1),流动相是甲醇(A)-0.1%磷酸水(B),柱温为30℃,...基于化学识别模式,建立HPLC指纹图谱及多成分含量测定,为金花三宝口服液的质量评价提供参考。采用HPLC法,以Capcell Pak C_(18)(250mm×4.6mm,5μm)为色谱柱,流速为0.8mL·min^(-1),流动相是甲醇(A)-0.1%磷酸水(B),柱温为30℃,进样量为10μL,检测波长为266nm;建立指纹图谱并同时测定6个成分的含量,采用聚类分析(CA),主成分分析(PCA)以及偏最小二乘-判别分析(PLS-DA)对数据进行分析。指纹图谱共确定了13个共有峰,并指认了6个成分;相似度值为0.958以上;聚类分析,主成分分析将样品划分为2类,偏最小二乘-判别分析筛选出峰12,新绿原酸,芦丁等8个质量差异标志成分。含量测定结果表明:10批金花三宝口服液中新绿原酸、绿原酸、隐绿原酸、异绿原酸A、芦丁、异绿原酸C的平均含量依次为:0.2183,0.8465,0.1644,0.3790,0.0749,0.1701mg·mL^(-1)。本研究所建立的HPLC指纹图谱及多成分的含量测定准确,稳定可行,可为质量控制提供参考依据。展开更多
建立不同产地佩兰药材中11个成分含量同步检测方法,并采用化学识别模式和灰色关联度分析(grey relational analysis,GRA)法对其质量差异性进行评价。收集8省18个批次佩兰样品,采用高效液相色谱法同时检测佩兰中芦丁、异鼠李素-3-O-芸香...建立不同产地佩兰药材中11个成分含量同步检测方法,并采用化学识别模式和灰色关联度分析(grey relational analysis,GRA)法对其质量差异性进行评价。收集8省18个批次佩兰样品,采用高效液相色谱法同时检测佩兰中芦丁、异鼠李素-3-O-芸香糖苷、木犀草素、槲皮素、蒲公英甾醇、β-谷甾醇、豆甾醇、延胡索酸、琥珀酸、1,2-苯并吡喃酮和泽兰内酯含量,建立佩兰多组分定量控制模式;采用化学识别模式和GRA法建立佩兰质量优劣评价模型,对其质量进行差异性评价。在构建的色谱条件下,外标法方法学验证符合《中华人民共和国药典》规定;一测多评法(quantitative analysis of multicomponents by single-marker,QAMS)与外标法检测结果基本无差异;化学识别模式结果提示18批样品聚为3类,芦丁、豆甾醇、木犀草素、槲皮素、1,2-苯并吡喃酮和β-谷甾醇可能是影响佩兰产品质量主要潜在标志物;GRA结果显示18批佩兰质量相对关联度分别为0.4185、0.4779、0.5446、0.5035、0.5099、0.5440、0.4868、0.3388、0.3548、0.3608、0.3298、0.2910、0.6186、0.6273、0.6447、0.6627、0.5462和0.5705,表明江苏、河北和山东产地佩兰整体质量较好。建立的同时测定佩兰中11种成分含量的QAMS法,操作便捷,结果准确;化学识别模式及GRA法客观全面,可用于不同产地佩兰的质量差异评价。展开更多
Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented ...Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.展开更多
In this paper, gas chromatography-mass spectrometry （GS-MS） was used to build the standard fingerprint of volatile oil from Rosa multiflora Thunb. from 12 different habitats. Fourteen components in the volatile oil ...In this paper, gas chromatography-mass spectrometry （GS-MS） was used to build the standard fingerprint of volatile oil from Rosa multiflora Thunb. from 12 different habitats. Fourteen components in the volatile oil were identified as the indicator components ofR. multiflora, of which one was selected as the standard. The GC analysis conditions used for fingerprinting afford a very good separating effect. The similarity of the 12 volatile oils from R. multiflora Thunb. was more than 0.84, and the precision, stability and repeatability of the fingerprints were quite good. It could be concluded that the fingerprints can be used as the standard and as a quality control method for medicinal materials from R. multiflora Thunb..展开更多
文摘基于化学识别模式,建立HPLC指纹图谱及多成分含量测定,为金花三宝口服液的质量评价提供参考。采用HPLC法,以Capcell Pak C_(18)(250mm×4.6mm,5μm)为色谱柱,流速为0.8mL·min^(-1),流动相是甲醇(A)-0.1%磷酸水(B),柱温为30℃,进样量为10μL,检测波长为266nm;建立指纹图谱并同时测定6个成分的含量,采用聚类分析(CA),主成分分析(PCA)以及偏最小二乘-判别分析(PLS-DA)对数据进行分析。指纹图谱共确定了13个共有峰,并指认了6个成分;相似度值为0.958以上;聚类分析,主成分分析将样品划分为2类,偏最小二乘-判别分析筛选出峰12,新绿原酸,芦丁等8个质量差异标志成分。含量测定结果表明:10批金花三宝口服液中新绿原酸、绿原酸、隐绿原酸、异绿原酸A、芦丁、异绿原酸C的平均含量依次为:0.2183,0.8465,0.1644,0.3790,0.0749,0.1701mg·mL^(-1)。本研究所建立的HPLC指纹图谱及多成分的含量测定准确,稳定可行,可为质量控制提供参考依据。
文摘建立不同产地佩兰药材中11个成分含量同步检测方法,并采用化学识别模式和灰色关联度分析(grey relational analysis,GRA)法对其质量差异性进行评价。收集8省18个批次佩兰样品,采用高效液相色谱法同时检测佩兰中芦丁、异鼠李素-3-O-芸香糖苷、木犀草素、槲皮素、蒲公英甾醇、β-谷甾醇、豆甾醇、延胡索酸、琥珀酸、1,2-苯并吡喃酮和泽兰内酯含量,建立佩兰多组分定量控制模式;采用化学识别模式和GRA法建立佩兰质量优劣评价模型,对其质量进行差异性评价。在构建的色谱条件下,外标法方法学验证符合《中华人民共和国药典》规定;一测多评法(quantitative analysis of multicomponents by single-marker,QAMS)与外标法检测结果基本无差异;化学识别模式结果提示18批样品聚为3类,芦丁、豆甾醇、木犀草素、槲皮素、1,2-苯并吡喃酮和β-谷甾醇可能是影响佩兰产品质量主要潜在标志物;GRA结果显示18批佩兰质量相对关联度分别为0.4185、0.4779、0.5446、0.5035、0.5099、0.5440、0.4868、0.3388、0.3548、0.3608、0.3298、0.2910、0.6186、0.6273、0.6447、0.6627、0.5462和0.5705,表明江苏、河北和山东产地佩兰整体质量较好。建立的同时测定佩兰中11种成分含量的QAMS法,操作便捷,结果准确;化学识别模式及GRA法客观全面,可用于不同产地佩兰的质量差异评价。
基金Supported by the National Natural Science Foundation of China (No. 30570485)the Shanghai "Chen Guang" Project (No. 09CG69).
文摘Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.
文摘In this paper, gas chromatography-mass spectrometry （GS-MS） was used to build the standard fingerprint of volatile oil from Rosa multiflora Thunb. from 12 different habitats. Fourteen components in the volatile oil were identified as the indicator components ofR. multiflora, of which one was selected as the standard. The GC analysis conditions used for fingerprinting afford a very good separating effect. The similarity of the 12 volatile oils from R. multiflora Thunb. was more than 0.84, and the precision, stability and repeatability of the fingerprints were quite good. It could be concluded that the fingerprints can be used as the standard and as a quality control method for medicinal materials from R. multiflora Thunb..
