【摘要】：通過實時采集正常操作條件下及發(fā)生異常工況時的甘草提取液的動態(tài)近紅外光譜數(shù)據(jù),結(jié)合主成分分析法(PCA)、偏最小二乘回歸法(PLSR)和平行因子-偏最小二乘回歸聯(lián)用法(PARAFAC-PLSR)建立3種甘草提取過程的實時監(jiān)測模型,并分析各種模型的特點。結(jié)果表明,基于3種方法建立的模型均能在一定程度上預(yù)測異常加熱工況的發(fā)生,但同時也存在一定誤判。其中,PCA方法建立的模型出錯率最高,在60 min之前就出現(xiàn)3次"故障"誤判,不適用于該過程的分析應(yīng)用。而PLSR和PARAFAC-PLSR模型基本效果相似,校正集相關(guān)系數(shù)分別高達0.934 2,0.928 1,驗證集相關(guān)系數(shù)也分別達到了0.856 7,0.828 3;并且這2種方法建立的預(yù)測模型誤判率較低,首次成功預(yù)測的故障均發(fā)生于75 min。此外,PLSR和PARAFAC-PLSR模型均能在一定程度上預(yù)測出系統(tǒng)狀態(tài)的走勢。說明基于動態(tài)近紅外光譜動態(tài)數(shù)據(jù)建立的PLSR和PARAFAC-PLSR模型均具有良好的在線監(jiān)測和預(yù)測功能,為中藥提取過程動態(tài)監(jiān)測方法的優(yōu)化選擇提供了參考依據(jù)。
[Abstract]:The dynamic near infrared spectrum data of Glycyrrhiza uralensis extract under normal operating conditions and abnormal operating conditions were collected in real time, combined with principal component analysis (PCA),). Partial least square regression (PLSR) and parallel factor-partial least squares regression (PARAFAC-PLSR) were used to establish real-time monitoring models for the extraction process of Glycyrrhiza uralensis, and the characteristics of the models were analyzed. The results show that the models based on the three methods can predict the abnormal heating condition to a certain extent, but there are also some misjudgments at the same time. Among them, the PCA method has the highest error rate and "fault" misjudgment occurs three times before 60 min, which is not suitable for the analysis and application of this process. The basic effect of PLSR and PARAFAC-PLSR models is similar, the correlation coefficient of correction set is as high as 0.934 ~ (22) 928 1, and the correlation coefficient of verification set is 0.856 ~ 7 ~ 0.828 ~ 3 respectively. The error rate of the two prediction models was low, and the first successful prediction occurred at 75 min.. In addition, both PLSR and PARAFAC-PLSR models can predict the trend of system state to some extent. It is concluded that both PLSR and PARAFAC-PLSR models based on dynamic near infrared spectrum data have good on-line monitoring and prediction functions, which provides a reference for the optimization and selection of dynamic monitoring methods in the process of traditional Chinese medicine extraction.
【作者單位】： 天津中醫(yī)藥大學(xué)中藥制藥工程學(xué)院;天津中醫(yī)藥大學(xué)現(xiàn)代中藥產(chǎn)業(yè)技術(shù)研究院;天津市現(xiàn)代中藥省部共建國家重點實驗室(培育);
【基金】：天津市科技創(chuàng)新體系及條件平臺建設(shè)計劃項目(14TXZYJC00440);天津市科技創(chuàng)新體系及條件平臺建設(shè)計劃項目(15PTCYSY00030)
【分類號】：R284.2;O657.33

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