XRF结合化学计量学对纸张快递文件袋的分类研究

Research on the Classification of Paper Express Document Bags by XRF and Chemometrics

本文旨在建立一种快速无损的识别纸质快递文件袋的分析方法。利用手持式X射线荧光光谱仪对50个纸质快递文件袋样品进行检验,通过分析样品的X射线光谱数据的元素种类和含量对样本进行分类。对归一化后的光谱数据分别建立贝叶斯判别、RBF神经网络和K-近邻三种分类模型,对分类结果进行分析验证。50个样品可被分为四类,贝叶斯判别模型、RBF神经网络和K-近邻分类模型的分类准确率分别为96%、98%和100%。通过对分类准确率的比较,K-近邻分类模型更适合此类样本的XRF光谱数据,XRF与化学计量学相结合可以对纸质快递文件袋实现有效的识别。该方法简单快速且无损样品,可为快递文件袋类物证鉴定提供科学依据。

To establish a fast and non-destructive analysis method for identifying paper express document bags.50 samples of paper delivery document bags were inspected by handheld X-ray fluorescence,and the samples were classified by analyzing the element types and contents of the X-ray spectrum data of the samples.Establish Bayesian discriminant analysis,RBF neural network,and K-nearest neighbor classification models for normalized spectral data,and analyze and verify the classification results.The 50 samples can be divided into four categories.The classification accuracy of Bayesian Discriminative model,RBF neural network and K-nearest neighbor classification model is 96%,98%and 100%respectively.By comparing the classification accuracy,the K-nearest neighbor classification model is more suitable for XRF spectral data of such samples.The combination of XRF and Chemometrics can effectively identify paper express document bags.The combination of XRF and Chemometrics can effectively identify paper express document bags.This method is simple,fast,and non-destructive for samples.It can provide scientific basis for the identification of physical evidence in express delivery document bags.