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。

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.

R283.6

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）