X射线荧光光谱结合BP神经网络对护手霜塑料包装瓶的分类研究  

Research on Plastic Packaging Identification of Hand Cream by X-ray Fluorescence Spectroscopy Based on BP Neural Network

作　　者：李冬怡 李春宇[1] 姜红 赵雪珺 满吉 Li DongYi;Li Chunyu;Jiang Hong;Zhao Xuejun;Man Ji(School of Investigation,People’s Public Security University of China,Beijing 100038,China;Center of Forensic Science Beijing Hui Zheng Zhuo Yue Technology Co.,Ltd.,Beijing 102446,China;Shanghai Key Laboratory of Crime Scene Evidence Co.,Ltd.,Shanghai 200072,China;Beijing Huayi Hongsheng Technology Co.,Ltd.,Beijing,Beijing 100123,China)

机构地区：[1]中国人民公安大学侦查学院,北京100038 [2]北京汇正卓越科技有限公司司法鉴定中心,北京102446 [3]上海市刑事科学技术研究院,上海200072 [4]北京华仪宏盛技术有限公司,北京100123

出　　处：《实验与分析》2025年第1期15-22,共8页LABOR PRAXIS

基　　金：上海市现场物证重点实验室开放课题基金资助(2024XCWZK02)。

摘　　要：针对护手霜塑料包装物证溯源需求,文章设计了X射线荧光光谱(XRF)结合深度学习的分类方法。通过XRF技术对52个护手霜包装样品进行无损检测,并对所得光谱数据进行z-score标准化处理,利用K-means聚类进行分类,借助Elbow原则和Silhouette分析确定了最优的聚类数目,发现四个聚类为最佳选择。构建反向传播神经网络(BPNN)和粒子群优化算法(PSO)后的BPNN模型验证分类结果可靠性。实验发现,单独使用BPNN模型在训练集上的准确率为96.4286%,在测试集上的准确率为91.6667%,而PSO-BPNN模型在训练集和测试集上的准确率均达到了100%。结果表明,结合X射线荧光光谱法与PSO优化的BPNN模型,能够有效地对护手霜包装进行精确分类,并为物证溯源提供更为科学的手段。This study addresses the traceability requirements of hand cream plastic packaging evidence by designing a classification method that combines X-ray fluorescence spectroscopy(XRF)with deep learning.Non-destructive testing was conducted on 52 hand cream packaging samples using XRF.The obtained spectral data were standardized using z-score normalization,and K-means clustering was employed for classification.The optimal number of clusters was determined using the Elbow method and Silhouette analysis,with four clusters identified as the best choice.The reliability of the classification results was validated by constructing a Backpropagation Neural Network(BPNN)model and a BPNN model optimized with Particle Swarm Optimization(PSO).Experimental results showed that the BPNN model alone achieved an accuracy of 96.4286%on the training set and 91.6667%on the test set,while the PSO-BPNN model achieved 100%accuracy on both the training and test sets.The results indicate that the combination of X-ray fluorescence spectroscopy and the PSO-optimized BPNN model can effectively classify hand cream packaging with high precision,providing a more scientific approach for evidence traceability.

关 键 词：护手霜包装 X射线荧光光谱 K-MEANS聚类 BP神经网络 粒子群优化 

分 类 号：O657.34[理学—分析化学]