目的 建立基于多变量统计过程控制（MSPC）技术的注射用益气复脉（冻干）麦冬水提取过程的在线监测方法，实现对麦冬水提取过程的实时监测。方法 以蒸汽压力、保沸温度、冷却水回水温度3个关键过程参数，结合近红外光谱技术在线监测的果糖水平为变量，采用商业化规模9个生产批次建立麦冬水提取过程的MSPC模型；使用SIMCA-P+14.1软件进行数据分析，使用偏最小二乘算法（PLS）进行自动拟合建立批次变化模型（BEM），用于生产过程评价；使用主成分分析（PCA）进行自动拟合建立批次水平模型（BLM），用于批次评价。将模型用于3个商业化规模实验批次（检验批1、2、3）的过程监测，评价模型性能。结果 生成BEM和BLM的HotellingT²图及DMod X控制图，DModX控制图采用＋3SD作为控制限，对各批次参数的数据结构（即各参数的相关关系）进行评价；HotellingT²图以95%作为控制限，在各批次参数的数据结构无差异的情况下，可对各批次数据是否存在异常进行评价。BLM结果显示检验批次的DMod X值超出控制限，BLM结果与BEM检验结果一致，检验批1部分时间节点的DMod X值超出控制限，检验批2和检验批3的大部分时间节点的DModX值超出控制限，对以上超限的数据点进行分析，发现原因主要为冷却水回水温度超出控制水平。结论 借助MSPC技术对复杂中药制造过程进行数据挖掘与模型开发，可实现对中药制药过程的实时监测，为中药智能控制技术的建立提供参考。

Objective To establish an online monitoring method for the extraction process of Ophiopogon japonicus in Yiqi Fumai Lyophilized Injection (YQFM), based on multivariate statistical process control (MSPC) technology, to realize real-time monitoring of the extraction process of Ophiopogon japonicus. Methods Using three key process parameters: steam pressure, boiling temperature, and cooling water return temperature, combined with the fructose level monitored online by near-infrared spectroscopy technology as variables, an MSPC model for the extraction process of winter wheat water was established using nine commercial production batches. Use SIMCA-P+14.1 software for data analysis, and use partial least squares (PLS) algorithm for automatic fitting to establish a batch change model (BEM) for production process evaluation. Use principal component analysis (PCA) algorithm for automatic fitting to establish a batch level model (BLM) for batch evaluation. Use the model for process monitoring of three commercial scale experimental batches (inspection batches 1, 2, and 3) to evaluate model performance. Results Generate Hotelling T² charts and DMod X control charts for BEM and BLM. The DMod X control chart uses +3SD as the control limit to evaluate the data structure (i.e. the correlation between parameters) of each batch of parameters. The Hotelling T² chart takes 95% as the control limit, and can evaluate whether there are any abnormalities in the data structure of each batch of parameters without any differences. The BLM results showed that the DMod X value of the inspection batch exceeded the control limit, and the BLM results were consistent with the BEM inspection results. The DMod X value of some time nodes in inspection batch 1 exceeded the control limit, while the DMod X value of most time nodes in inspection batch 2 and 3 exceeded the control limit. Analysis of the above exceeding data points revealed that the main reason was that the return water temperature of the cooling water exceeded the control level. Conclusion The results of this study show that the data mining and model development of complex traditional Chinese medicine manufacturing process with the help of statistical process control technology can realize the real-time monitoring of traditional Chinese medicine pharmaceutical process, and provide a reference for the establishment of intelligent control technology of traditional Chinese medicine.

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