目的 建立基于多變量統(tǒng)計過程控制（MSPC）技術的注射用益氣復脈（凍干）麥冬水提取過程的在線監(jiān)測方法，實現(xiàn)對麥冬水提取過程的實時監(jiān)測。方法 以蒸汽壓力、保沸溫度、冷卻水回水溫度3個關鍵過程參數(shù)，結合近紅外光譜技術在線監(jiān)測的果糖水平為變量，采用商業(yè)化規(guī)模9個生產(chǎn)批次建立麥冬水提取過程的MSPC模型；使用SIMCA-P+14.1軟件進行數(shù)據(jù)分析，使用偏最小二乘算法（PLS）進行自動擬合建立批次變化模型（BEM），用于生產(chǎn)過程評價；使用主成分分析（PCA）進行自動擬合建立批次水平模型（BLM），用于批次評價。將模型用于3個商業(yè)化規(guī)模實驗批次（檢驗批1、2、3）的過程監(jiān)測，評價模型性能。結果 生成BEM和BLM的HotellingT²圖及DMod X控制圖，DModX控制圖采用＋3SD作為控制限，對各批次參數(shù)的數(shù)據(jù)結構（即各參數(shù)的相關關系）進行評價；HotellingT²圖以95%作為控制限，在各批次參數(shù)的數(shù)據(jù)結構無差異的情況下，可對各批次數(shù)據(jù)是否存在異常進行評價。BLM結果顯示檢驗批次的DMod X值超出控制限，BLM結果與BEM檢驗結果一致，檢驗批1部分時間節(jié)點的DMod X值超出控制限，檢驗批2和檢驗批3的大部分時間節(jié)點的DModX值超出控制限，對以上超限的數(shù)據(jù)點進行分析，發(fā)現(xiàn)原因主要為冷卻水回水溫度超出控制水平。結論 借助MSPC技術對復雜中藥制造過程進行數(shù)據(jù)挖掘與模型開發(fā)，可實現(xiàn)對中藥制藥過程的實時監(jiān)測，為中藥智能控制技術的建立提供參考。

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.

R284.2