Article(id=1304406851395801730, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304406828071281069, articleNumber=null, orderNo=null, doi=10.7501/j.issn.0253-2670.2026.02.007, pmid=null, cstr=null, oa=null, hot=0, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1754582400000, receivedDateStr=2025-08-08, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1788924428080, onlineDateStr=2026-09-09, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1788924428080, onlineIssueDateStr=2026-09-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1788924428080, creator=13701087609, updateTime=1788924428080, updator=13701087609, issue=Issue{id=1304406828071281069, tenantId=1146029695717560320, journalId=1302319053441957962, year='2026', volume='57', issue='2', pageStart='393', pageEnd='788', issueExtLink='null', onlineDate='null', pubDate='1769529600000', pubDateStr='2026-01-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1788924422518, creator='13701087609', updateTime=1788924652596, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1304407793138688830, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304406828071281069, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1304407793138688831, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1304406828071281069, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=448, endPage=456, ext={EN=ArticleExt(id=1304406851693597316, articleId=1304406851395801730, tenantId=1146029695717560320, journalId=1302319053441957962, language=EN, title=Prediction of total solid content and effective components in Jinzhen Oral Liquid based on physical properties, columnId=null, journalTitle=Chinese Traditional and Herbal Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=Objective To investigate the relationship between the physical properties of Jinzhen Oral Liquid (金振口服液) intermediates and their total solid content (TSC) and the contents of effective components, and to establish a content prediction model, thereby providing a reference for rapid quality evaluation of intermediates. Methods Samples were collected from two types of extracts (mineral-botanical and artificial cow bezoar) and four key manufacturing steps (hot mixing, refrigeration, centrifugation, and filling). Six physical parameters (density, viscosity, surface tension, electrical conductivity, refractive index, and pH value) were measured, alongside the contents of six effective components (baicalin, wogonoside, glycyrrhizic acid, gallic acid, hyodeoxycholic acid, and cholic acid) and TSC. Correlation analysis was performed, and polynomial regression models were developed for the overall process, the mineral-botanical extract, and the artificial cow bezoar extract, respectively. Results Correlation analysis indicated that most physical parameters were significantly correlated with chemical components throughout the entire preparation process. Refractive index showed a strong correlation with TSC (r = 0.845), while density was correlated with TSC and baicalin with coefficients of 0.529 and 0.505, respectively. In mineral-botanical extracts, the correlation coefficient between refractive index and TSC was 0.525. In artificial cow bezoar extracts, refractive index was correlated with TSC, hyodeoxycholic acid, and cholic acid, with coefficients of 0.759, 0.729, and 0.593, respectively. The regression models established based on refractive index performed well in validation: the model for TSC in the full process showed a mean relative error (MRE) of 4.04% and a mean absolute error (MAE) of 1.10%. The MRE values for the baicalin and wogonoside models were both below 10%, and both passed the F -test. The MRE values for the TSC models in both mineral-botanical and artificial cow bezoar extracts were below 6%. Conclusion The refractive index of Jinzhen Oral Liquid intermediates shows significant correlation with both TSC and the contents of effective components. The refractive index method is a feasible approach for predicting TSC and the contents of baicalin and wogonoside during the manufacturing process, as well as TSC in both extracts. This study provides a novel strategy for the rapid quality assessment of Jinzhen Oral Liquid production and offers a technical basis for the practical application of online refractometry., authors=CHEN Xuebin, XU Fangfang, ZHANG Yongchao, LIU Jiali, LI Zhidong, ZHANG Chenfeng, WANG Zhenzhong, LI Xiumei, ZHANG Xin, authorsList=CHEN Xuebin, XU Fangfang, ZHANG Yongchao, LIU Jiali, LI Zhidong, ZHANG Chenfeng, WANG Zhenzhong, LI Xiumei, ZHANG Xin, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1304406851613905539, articleId=1304406851395801730, tenantId=1146029695717560320, journalId=1302319053441957962, language=CN, title=基于物性参数的金振口服液总固体量和功效物质含量预测研究, columnId=1304140189132149234, journalTitle=中草药, columnName=药剂与工艺, runingTitle=null, highlight=null, articleAbstract=目的 探究金振口服液（Jinzhen Oral Liquid）中间体的物性参数与总固体量及功效物质含量之间的关系并建立含量预测模型，为中间体的快速质量评价提供参考。方法 收集矿植物、人工牛黄2种浸膏及制剂过程的热配、冷藏、离心和灌装4个关键工序的样本，测定各中间体的密度、黏度、表面张力、电导率、折光率和pH值6个物性参数，以及黄芩苷、汉黄芩苷、甘草酸、没食子酸、猪去氧胆酸和胆酸6个功效物质的含量及总固体量，对物性参数与功效物质含量及总固体量进行相关性分析，并采用多项式回归方法，分别构建适用于制剂过程、矿植物浸膏和人工牛黄浸膏的回归模型。结果 相关性分析结果表明，在制剂全过程中，多数物性参数与化学成分呈显著性相关，折光率与总固体量高度相关（r ＝0.845），密度与总固体量及黄芩苷的相关系数分别为0.529和0.505。在矿植物浸膏中，折光率与总固体量的相关系数为0.525；在人工牛黄浸膏中，折光率与总固体量、猪去氧胆酸及胆酸的相关系数分别为0.759、0.729和0.593。基于折光率构建的回归模型在验证实验中表现良好：制剂全过程总固体量模型的平均相对误差（mean relative error，MRE）和平均绝对误差（mean absolute error，MAE）分别为4.04%和1.10%；黄芩苷与汉黄芩苷模型的MRE均低于10%，且通过F 检验；矿植物浸膏与人工牛黄浸膏中，总固体量模型的MRE均低于6%。结论 金振口服液中间体的折光率与关键功效成分含量及总固体量均具有显著相关性，采用折光率法预测制剂过程中的总固体量、黄芩苷和汉黄芩苷含量以及2种浸膏的总固体量具有可行性，为金振口服液生产过程的快速质量评价提供新方法，并为在线折光技术的实际应用提供技术依据。, authors=陈学斌1,2 , 徐芳芳1,2 , 张永超1,2 , 刘佳丽1,2 , 李执栋1,2 , 章晨峰1,2 , 王振中1,2 , 李秀梅1,2 , 张欣1,2 , authorsList=陈学斌, 徐芳芳, 张永超, 刘佳丽, 李执栋, 章晨峰, 王振中, 李秀梅, 张欣, authorCompany=1 中药制药过程控制与智能制造技术全国重点实验室, 江苏 连云港 222001; et al . Gallic acid attenuates allergic airway inflammation via suppressed interleukin-33 and group 2 innate lymphoid cells in ovalbumin-induced asthma in mice[J]. Int Forum Allergy Rhinol , 2018, 8(11):1284-1290.et al . Recent progress in bile acid-based antimicrobials[J]. Bioconjug Chem , 2021, 32(3):395-410.et al . Cholic acid-based antimicrobial peptide mimics as antibacterial agents[J]. Int J Mol Sci , 2022, 23(9):4623.et al . Cholic acid attenuates ER stress-induced cell death in coxsackievirus-B3 infection[J]. J Microbiol Biotechnol , 2018, 28(1):109-114.et al . Cholic acid:A novel steroidal uncompetitive inhibitor against β-lactamase produced by multidrug-resistant isolates[J]. World J Microbiol Biotechnol , 2021, 37(9):152.
中草药
|药剂与工艺
2026
, 57
(2) :
448
-456
基于物性参数的金振口服液总固体量和功效物质含量预测研究
全屏
[Author(id=1307423891761164316, tenantId=1146029695717560320, journalId=null, articleId=1304406851395801730, orderNo=null, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=null, email=null, emailSecond=null, emailThird=null, correspondingAuthor=null, authorType=null, ext={EN=AuthorExt(id=null, tenantId=null, journalId=1302319053441957962, articleId=1304406851395801730, authorId=1307423891761164316, language=EN, stringName=CHEN Xuebin, XU Fangfang, ZHANG Yongchao, LIU Jiali, LI Zhidong, ZHANG Chenfeng, WANG Zhenzhong, LI Xiumei, ZHANG Xin, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=null, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=null, tenantId=null, journalId=1302319053441957962, articleId=1304406851395801730, authorId=1307423891761164316, language=CN, stringName=陈学斌, 徐芳芳, 张永超, 刘佳丽, 李执栋, 章晨峰, 王振中, 李秀梅, 张欣, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=null, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=null)]
陈学斌, 徐芳芳, 张永超, 刘佳丽, 李执栋, 章晨峰, 王振中, 李秀梅, 张欣
作者信息
通讯作者:
李秀梅
作者简介:
陈学斌: 陈学斌，硕士，研究方向为经济管理与质量管理研究。E-mail: cxbzg@126.com
徐芳芳: 张欣，博士，研究方向为中药制药过程新技术。E-mail: zxtcm@126.com
Prediction of total solid content and effective components in Jinzhen Oral Liquid based on physical properties
CHEN Xuebin, XU Fangfang, ZHANG Yongchao, LIU Jiali, LI Zhidong, ZHANG Chenfeng, WANG Zhenzhong, LI Xiumei, ZHANG Xin
Affiliations
doi: 10.7501/j.issn.0253-2670.2026.02.007
文章导航
目的 探究金振口服液（Jinzhen Oral Liquid）中间体的物性参数与总固体量及功效物质含量之间的关系并建立含量预测模型，为中间体的快速质量评价提供参考。方法 收集矿植物、人工牛黄2种浸膏及制剂过程的热配、冷藏、离心和灌装4个关键工序的样本，测定各中间体的密度、黏度、表面张力、电导率、折光率和pH值6个物性参数，以及黄芩苷、汉黄芩苷、甘草酸、没食子酸、猪去氧胆酸和胆酸6个功效物质的含量及总固体量，对物性参数与功效物质含量及总固体量进行相关性分析，并采用多项式回归方法，分别构建适用于制剂过程、矿植物浸膏和人工牛黄浸膏的回归模型。结果 相关性分析结果表明，在制剂全过程中，多数物性参数与化学成分呈显著性相关，折光率与总固体量高度相关（r ＝0.845），密度与总固体量及黄芩苷的相关系数分别为0.529和0.505。在矿植物浸膏中，折光率与总固体量的相关系数为0.525；在人工牛黄浸膏中，折光率与总固体量、猪去氧胆酸及胆酸的相关系数分别为0.759、0.729和0.593。基于折光率构建的回归模型在验证实验中表现良好：制剂全过程总固体量模型的平均相对误差（mean relative error，MRE）和平均绝对误差（mean absolute error，MAE）分别为4.04%和1.10%；黄芩苷与汉黄芩苷模型的MRE均低于10%，且通过F 检验；矿植物浸膏与人工牛黄浸膏中，总固体量模型的MRE均低于6%。结论 金振口服液中间体的折光率与关键功效成分含量及总固体量均具有显著相关性，采用折光率法预测制剂过程中的总固体量、黄芩苷和汉黄芩苷含量以及2种浸膏的总固体量具有可行性，为金振口服液生产过程的快速质量评价提供新方法，并为在线折光技术的实际应用提供技术依据。
金振口服液
/
物性参数
/
功效物质
/
总固体量
/
快速质量评价
/
折光率
/
黄芩苷
/
汉黄芩苷
/
甘草酸
/
没食子酸
/
猪去氧胆酸
/
胆酸
Objective To investigate the relationship between the physical properties of Jinzhen Oral Liquid (金振口服液) intermediates and their total solid content (TSC) and the contents of effective components, and to establish a content prediction model, thereby providing a reference for rapid quality evaluation of intermediates. Methods Samples were collected from two types of extracts (mineral-botanical and artificial cow bezoar) and four key manufacturing steps (hot mixing, refrigeration, centrifugation, and filling). Six physical parameters (density, viscosity, surface tension, electrical conductivity, refractive index, and pH value) were measured, alongside the contents of six effective components (baicalin, wogonoside, glycyrrhizic acid, gallic acid, hyodeoxycholic acid, and cholic acid) and TSC. Correlation analysis was performed, and polynomial regression models were developed for the overall process, the mineral-botanical extract, and the artificial cow bezoar extract, respectively. Results Correlation analysis indicated that most physical parameters were significantly correlated with chemical components throughout the entire preparation process. Refractive index showed a strong correlation with TSC (r = 0.845), while density was correlated with TSC and baicalin with coefficients of 0.529 and 0.505, respectively. In mineral-botanical extracts, the correlation coefficient between refractive index and TSC was 0.525. In artificial cow bezoar extracts, refractive index was correlated with TSC, hyodeoxycholic acid, and cholic acid, with coefficients of 0.759, 0.729, and 0.593, respectively. The regression models established based on refractive index performed well in validation: the model for TSC in the full process showed a mean relative error (MRE) of 4.04% and a mean absolute error (MAE) of 1.10%. The MRE values for the baicalin and wogonoside models were both below 10%, and both passed the F -test. The MRE values for the TSC models in both mineral-botanical and artificial cow bezoar extracts were below 6%. Conclusion The refractive index of Jinzhen Oral Liquid intermediates shows significant correlation with both TSC and the contents of effective components. The refractive index method is a feasible approach for predicting TSC and the contents of baicalin and wogonoside during the manufacturing process, as well as TSC in both extracts. This study provides a novel strategy for the rapid quality assessment of Jinzhen Oral Liquid production and offers a technical basis for the practical application of online refractometry.
Jinzhen Oral Liquid
/
physical properties
/
effective components
/
total solid content
/
rapid quality evaluation
/
refractive index
/
baicalin
/
wogonoside
/
glycyrrhizic acid
/
gallic acid
/
hyodeoxycholic acid
/
cholic acid
陈学斌, 徐芳芳, 张永超, 刘佳丽, 李执栋, 章晨峰, 王振中, 李秀梅, 张欣.
基于物性参数的金振口服液总固体量和功效物质含量预测研究.
中草药,
2026
, 57
(2)
: 448
-456
.
DOI: 10.7501/j.issn.0253-2670.2026.02.007
CHEN Xuebin, XU Fangfang, ZHANG Yongchao, LIU Jiali, LI Zhidong, ZHANG Chenfeng, WANG Zhenzhong, LI Xiumei, ZHANG Xin.
Prediction of total solid content and effective components in Jinzhen Oral Liquid based on physical properties[J].
Chinese Traditional and Herbal Drugs ,
2026
, 57
(2)
: 448
-456
.
DOI: 10.7501/j.issn.0253-2670.2026.02.007
国家工信部产业基础再造和制造业高质量发展专项 (TC2308068); 国家长三角科技创新共同体联合攻关项目 (2023CSJGG1700); 中药制药过程控制与智能制造技术全国重点实验室开放基金课题 (SKL2023D02003)
参考文献
引证文献
张嘉颖, 李海波, 刘苓娴, 等. 基于UPLC-Q/TOF-MS的金振口服液在大鼠体内外源物表征[J]. 中草药, 2022, 53(10):2956-2967.et al . Gallic acid attenuates allergic airway inflammation via suppressed interleukin-33 and group 2 innate lymphoid cells in ovalbumin-induced asthma in mice[J]. Int Forum Allergy Rhinol , 2018, 8(11):1284-1290.et al . Recent progress in bile acid-based antimicrobials[J]. Bioconjug Chem , 2021, 32(3):395-410.et al . Cholic acid-based antimicrobial peptide mimics as antibacterial agents[J]. Int J Mol Sci , 2022, 23(9):4623.et al . Cholic acid attenuates ER stress-induced cell death in coxsackievirus-B3 infection[J]. J Microbiol Biotechnol , 2018, 28(1):109-114.et al . Cholic acid:A novel steroidal uncompetitive inhibitor against β-lactamase produced by multidrug-resistant isolates[J]. World J Microbiol Biotechnol , 2021, 37(9):152.
2026年第57卷第2期
PDF下载
24
8
引用本文
BibTeX
文章信息
doi: 10.7501/j.issn.0253-2670.2026.02.007
接收时间：2025-08-08
首发时间：2026-09-09
https://castjournals.cast.org.cn/joweb/zcy/CN/10.7501/j.issn.0253-2670.2026.02.007
引用本文
BibTeX
2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
关闭全屏
BibTeX
EndNote
RefWorks
TxT
PDF下载
全屏
24
8
关闭全屏
北京市海淀区学院南路86号
010-62199257（仅加盟咨询）
qkjq@cast.org.cn
