基于现场通用模型的3种蜜炙饮片水分属性动态在线监测方法研究

Research on dynamic on-line monitoring method of moisture attribute in three honey-processed Chinese herbal slice based on in-situ general model

针对中药制造过程原料单元蜜炙饮片水分含量关键质量属性离线静态检测具有滞后性及破坏性等问题,以蜜炙款冬、蜜炙黄芪、蜜炙甘草为载体,采用烘干法测定水分含量作为参考值,采用运动载物台模拟实际现场传输带生产过程样品运动过程,采集近红外(near infrared,NIR)光谱,结合机器学习方法,建立多品种蜜炙饮片水分含量NIR现场动态检测模型。结果表明,采用二阶导数法对光谱进行预处理,决策树数量(ntree)、随机特征数量(max feature)、生成叶节点的最小样本数(node size)分别选择46、76、8时,建立的水分含量定量分析模型效果最优。模型的预测决定系数(the prediction coefficient of determination,R2pre)及预测均方根误差(root mean square error of prediction,RMSEP)分别为0.9032和0.3302。本研究建立的多品种蜜炙饮片水分含量NIR通用定量模型具有良好的预测性能,可实现同时对蜜炙款冬、蜜炙黄芪、蜜炙甘草水分含量的快速检测,为中药制造过程原料单元蜜炙饮片的水分测定提供方法。

Aiming at the hysteresis and destructiveness of off-line static detection of critical quality attribute of the moisture content of the raw material unit of the traditional Chinese medicine manufacturing process,honey-processed Tussilago farfara,honey-processed Astragalus and honey-processed Glycyrrhiza uralensis were used as the research carriers,and the drying method was used to measure the moisture content as a reference value.The moving stage was used to simulate the movement process of samples on the conveyor belt in the actual on-site production process,and near-infrared(NIR)spectra were collected,combined with machine learning,to establish NIR on-site dynamic detection model of moisture content in multi-variety honey-processed Chinese herbal slice.The results show that the second derivative method is used to preprocess the spectrum.The number of decision trees(ntree),the number of random features(max feature),and the minimum number of samples for generating leaf nodes(node size)are selected:46,76,and 8,respectively.The quantitative analysis model of moisture content has the best effect.The prediction coefficient of determination(the prediction coefficient of determination,R°pre)ore and the root mean square error of prediction(root mean square error of prediction,RMSEP)of the model were 0.9032 and 0.3302,respectively.The NIR quantitative model for the moisture content of multi-variety honey-processed Chinese herbal slice established in this study has good predictive performance,and can achieve rapid,accurate and non-destructive quantitative analysis of the moisture content of honey-processed Tussilago farfara,honey-processed Astragalus and honey-processed Glycyrrhiza uralensis at the same time,and provides a method for determining the moisture content of honey-processed Chinese herbal slice of the raw material unit of the traditional Chinese medicine manufacturing process.