目的 建立基于多變量統(tǒng)計(jì)過程控制（MSPC）技術(shù)的注射用益氣復(fù)脈（凍干）麥冬水提取過程的在線監(jiān)測(cè)方法，實(shí)現(xiàn)對(duì)麥冬水提取過程的實(shí)時(shí)監(jiān)測(cè)。方法 以蒸汽壓力、保沸溫度、冷卻水回水溫度3個(gè)關(guān)鍵過程參數(shù)，結(jié)合近紅外光譜技術(shù)在線監(jiān)測(cè)的果糖水平為變量，采用商業(yè)化規(guī)模9個(gè)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(jìn)行數(shù)據(jù)分析，使用偏最小二乘算法（PLS）進(jìn)行自動(dòng)擬合建立批次變化模型（BEM），用于生產(chǎn)過程評(píng)價(jià)；使用主成分分析（PCA）進(jìn)行自動(dòng)擬合建立批次水平模型（BLM），用于批次評(píng)價(jià)。將模型用于3個(gè)商業(yè)化規(guī)模實(shí)驗(yàn)批次（檢驗(yàn)批1、2、3）的過程監(jiān)測(cè)，評(píng)價(jià)模型性能。結(jié)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(duì)各批次參數(shù)的數(shù)據(jù)結(jié)構(gòu)（即各參數(shù)的相關(guān)關(guān)系）進(jìn)行評(píng)價(jià)；HotellingT²圖以95%作為控制限，在各批次參數(shù)的數(shù)據(jù)結(jié)構(gòu)無差異的情況下，可對(duì)各批次數(shù)據(jù)是否存在異常進(jìn)行評(píng)價(jià)。BLM結(jié)果顯示檢驗(yàn)批次的DMod X值超出控制限，BLM結(jié)果與BEM檢驗(yàn)結(jié)果一致，檢驗(yàn)批1部分時(shí)間節(jié)點(diǎn)的DMod X值超出控制限，檢驗(yàn)批2和檢驗(yàn)批3的大部分時(shí)間節(jié)點(diǎn)的DModX值超出控制限，對(duì)以上超限的數(shù)據(jù)點(diǎn)進(jìn)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(jié)論 借助MSPC技術(shù)對(duì)復(fù)雜中藥制造過程進(jìn)行數(shù)據(jù)挖掘與模型開發(fā)，可實(shí)現(xiàn)對(duì)中藥制藥過程的實(shí)時(shí)監(jiān)測(cè)，為中藥智能控制技術(shù)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