基于SOM-FDA利用XRF对药品铝塑包装片的分类

Study on classification of aluminum plastic packaging tablets for drugs based on SOM-FDA using XRF spectroscopy

建立了一种对药品铝塑包装片进行快速分类的方法。利用能量色散型X射线荧光光谱(XRF)仪,对47种不同的药品铝塑包装片样品进行了检验,结合自组织映射(self organizing map,SOM)神经网络聚类,通过最大相关性最小冗余(maximum relevance minimum redundancy,MRMR)算法对元素重要性进行排序,并利用最近邻(K-nearest neighbor,KNN)分类器处理样品数据。依据样品中所含元素的种类及质量分数的不同,对药品铝塑包装片进行区分。SOM神经网络聚类的结果为9类,KNN分类器的准确率为97.87%。X射线荧光光谱法操作简便快速、无损检材、灵敏度高。建立的分类模型科学准确,可为公安机关大规模筛选、确定侦查方向、缩短侦查时间提供帮助。

This paper presents establish a method for rapid classification of pharmaceutical aluminum plastic packaging sheets.Energy dispersive X-ray fluorescence spectrometer(XRF)was used to inspect aluminum plastic packaging samples from 47 different drugs.The importance of elements were sorted by maximum relevance minimum redundancy(MRMR)algorithm combined with a self-organizing map(SOM)network clustering,and K-nearest neighbor(KNN)was used to process sample data.The aluminum-plastic packaging sheets of pharmaceutical products can be differentiated based on the types and contents of elements contained in the samples.The clustering result of SOM neural network indicated 9 categories,and the accuracy of the KNN classifier was 97.87%.It shows that X-ray fluorescence spectroscopy is a simple,fast,non-destructive,and highly sensitive method for analyzing materials.The established classification model is scientifically accurate and can provide assistance for public security organs in largescale screening,determining investigation directions,and shortening investigation time.