目的:建立注射用苦碟子HPLC数字化指纹图谱,为注射用苦碟子质量控制提供依据。方法:采用反相HPLC法分析注射用苦碟子水溶液,以Century SIL BDS柱(250mm×4.6mm,5μm),流动相为1%醋酸水-1%醋酸乙腈低压梯度洗脱,检测波长265nm,柱温(3...目的:建立注射用苦碟子HPLC数字化指纹图谱,为注射用苦碟子质量控制提供依据。方法:采用反相HPLC法分析注射用苦碟子水溶液,以Century SIL BDS柱(250mm×4.6mm,5μm),流动相为1%醋酸水-1%醋酸乙腈低压梯度洗脱,检测波长265nm,柱温(30±0.15)℃,进样量20μL。用"中药色谱指纹图谱超信息特征数字化评价系统3.0"软件计算不同批次的注射用苦碟子HPLC指纹图谱的色谱指纹图谱指数F和色谱指纹图分离量指数RF等42个参数进行潜信息特征数字化评价。以双参照物体系结合定量结构-性质相关(QSPR)原理标定指纹峰的表观分子量、峰位、洗脱动量数δ、折合相对积分φ。结果:以咖啡酸峰为参照物峰,确定21个共有峰,建立了注射用苦碟子HPLC数字化指纹图谱,获得了判别注射用苦碟子质量的重要数字化信息。以双定性双定量相似度法评价注射用苦碟子批间质量稳定。结论:所建立HPLC数字化指纹图谱具有较好的精密度和重现性,适用于注射用苦碟子的质量控制。数字化指纹图谱是数字中药的核心技术,双定性双定量相似度法是宏观定性定量评价中药质量的最准确和最佳技术。展开更多
目的建立丹参水溶性成分HPLC数字化指纹图谱,为丹参质量控制提供依据。方法采用RP-HPLC法分析丹参水溶液,以Century SIL BDS柱(250mm×4.6mm,5μm),流动相为1%醋酸水-1%醋酸甲醇低压梯度洗脱,检测波长:290nm,柱温:(30±0.15)℃...目的建立丹参水溶性成分HPLC数字化指纹图谱,为丹参质量控制提供依据。方法采用RP-HPLC法分析丹参水溶液,以Century SIL BDS柱(250mm×4.6mm,5μm),流动相为1%醋酸水-1%醋酸甲醇低压梯度洗脱,检测波长:290nm,柱温:(30±0.15)℃,进样量5μL。用"中药色谱指纹图谱超信息特征数字化评价系统3.0"软件计算不同批次的丹参水溶性成分HPLC指纹图谱的色谱指纹图谱指数F和色谱指纹图分离量指数RF等46个参数进行潜信息特征数字化评价。以双参照物体系结合定量结构-性质相关(QSPR)原理标定指纹峰的表观分子量、峰位、洗脱动量数δ、折合相对积分ф。结果以丹酚酸B(SAB)峰为参照物峰,确定36个共有峰,建立了丹参水溶性成分HPLC数字化指纹图谱,获得了判别丹参药材质量的重要数字信息。以双定性双定量相似度法评价不同产地丹参药材,质量稳定。结论所建立HPLC数字化指纹图谱具有较好的精密度和重现性,适用于丹参药材的质量控制。数字化指纹图谱是数字中药的核心技术,双定性双定量相似度法是宏观定性定量评价中药质量的最准确、最佳技术。展开更多
With the arrival of the big data era,the phenomenon of information overload is becoming increasingly severe.In response to the common issue of sparse user rating matrices in recommendation systems,a collaborative filt...With the arrival of the big data era,the phenomenon of information overload is becoming increasingly severe.In response to the common issue of sparse user rating matrices in recommendation systems,a collaborative filtering recommendation algorithm was proposed based on improved user profiles in this study.Firstly,a profile labeling system was constructed based on user characteristics.This study proposed that user profile labels should be created using basic user information and basic item information,in order to construct multidimensional user profiles.TF-IDF algorithm was used to determine the weights of user-item feature labels.Secondly,user similarity was calculated by weighting both profile-based collaborative filtering and user-based collaborative filtering algorithms,and the final user similarity was obtained by harmonizing these weights.Finally,personalized recommendations were generated using Top-N method.Validation with the MovieLens-1M dataset revealed that this algorithm enhances both recommendation Precision and Recall compared to single-method approaches(recommendation algorithm based on user portrait and user-based collaborative filtering algorithm).展开更多
文摘目的:建立注射用苦碟子HPLC数字化指纹图谱,为注射用苦碟子质量控制提供依据。方法:采用反相HPLC法分析注射用苦碟子水溶液,以Century SIL BDS柱(250mm×4.6mm,5μm),流动相为1%醋酸水-1%醋酸乙腈低压梯度洗脱,检测波长265nm,柱温(30±0.15)℃,进样量20μL。用"中药色谱指纹图谱超信息特征数字化评价系统3.0"软件计算不同批次的注射用苦碟子HPLC指纹图谱的色谱指纹图谱指数F和色谱指纹图分离量指数RF等42个参数进行潜信息特征数字化评价。以双参照物体系结合定量结构-性质相关(QSPR)原理标定指纹峰的表观分子量、峰位、洗脱动量数δ、折合相对积分φ。结果:以咖啡酸峰为参照物峰,确定21个共有峰,建立了注射用苦碟子HPLC数字化指纹图谱,获得了判别注射用苦碟子质量的重要数字化信息。以双定性双定量相似度法评价注射用苦碟子批间质量稳定。结论:所建立HPLC数字化指纹图谱具有较好的精密度和重现性,适用于注射用苦碟子的质量控制。数字化指纹图谱是数字中药的核心技术,双定性双定量相似度法是宏观定性定量评价中药质量的最准确和最佳技术。
文摘目的建立丹参水溶性成分HPLC数字化指纹图谱,为丹参质量控制提供依据。方法采用RP-HPLC法分析丹参水溶液,以Century SIL BDS柱(250mm×4.6mm,5μm),流动相为1%醋酸水-1%醋酸甲醇低压梯度洗脱,检测波长:290nm,柱温:(30±0.15)℃,进样量5μL。用"中药色谱指纹图谱超信息特征数字化评价系统3.0"软件计算不同批次的丹参水溶性成分HPLC指纹图谱的色谱指纹图谱指数F和色谱指纹图分离量指数RF等46个参数进行潜信息特征数字化评价。以双参照物体系结合定量结构-性质相关(QSPR)原理标定指纹峰的表观分子量、峰位、洗脱动量数δ、折合相对积分ф。结果以丹酚酸B(SAB)峰为参照物峰,确定36个共有峰,建立了丹参水溶性成分HPLC数字化指纹图谱,获得了判别丹参药材质量的重要数字信息。以双定性双定量相似度法评价不同产地丹参药材,质量稳定。结论所建立HPLC数字化指纹图谱具有较好的精密度和重现性,适用于丹参药材的质量控制。数字化指纹图谱是数字中药的核心技术,双定性双定量相似度法是宏观定性定量评价中药质量的最准确、最佳技术。
文摘With the arrival of the big data era,the phenomenon of information overload is becoming increasingly severe.In response to the common issue of sparse user rating matrices in recommendation systems,a collaborative filtering recommendation algorithm was proposed based on improved user profiles in this study.Firstly,a profile labeling system was constructed based on user characteristics.This study proposed that user profile labels should be created using basic user information and basic item information,in order to construct multidimensional user profiles.TF-IDF algorithm was used to determine the weights of user-item feature labels.Secondly,user similarity was calculated by weighting both profile-based collaborative filtering and user-based collaborative filtering algorithms,and the final user similarity was obtained by harmonizing these weights.Finally,personalized recommendations were generated using Top-N method.Validation with the MovieLens-1M dataset revealed that this algorithm enhances both recommendation Precision and Recall compared to single-method approaches(recommendation algorithm based on user portrait and user-based collaborative filtering algorithm).
