红外光谱法结合K-均值聚类与神经网络对饮料瓶的检验研究  

Study on Infrared Spectroscopy Combined with K⁃means Clustering and Neural Network for Beverage Bottle Inspection

作　　者：付钧泽 张嘉楠[1] 姜红[1] FU Junze;ZHANG Jianan;JIANG Hong(Institute of Criminal Investigation,People's Public Security University of China,Beijing 100038,China)

机构地区：[1]中国人民公安大学侦查学院,北京100038

出　　处：《中国塑料》2021年第1期91-97,共7页China Plastics

基　　金：中国人民公安大学2019年基科费重点项目(2019JKF222)。

摘　　要：利用傅里叶变换红外光谱法对41个不同品牌的塑料饮料瓶进行快速无损检测。谱图数据在经过预处理后可将样品分为聚对苯二甲酸乙二醇酯和聚乙烯两类。每一类内部的各个样品红外特征峰存在差异。对于数量最多的一类样品,通过主成分分析将样品光谱数据降维并提取主成分,然后结合K-均值聚类对样品进一步分组。最后以聚类结果作为因变量,构建神经网络算法对数据进行训练,用来预测样品分类情况。借助随机数发生器,随机选取86.5%的样品作为训练集,13.5%的样品作为测试集。结果表明,训练集和测试集的正确率均达到了100%,同时也验证了K-均值聚类结果的准确性,建立了塑料饮料瓶的快速分类模型;此分类模型方法可操作性好,结果准确可靠,为公安基层实际办案提供了参考。Plastic beverage bottles are a type of common material evidence at the scene of the crime.Fourier-transform infrared spectroscopy(FTIR)was used to detect 41 different brands of plastic beverage bottles.After pretreatment,the samples were divided into polyethylene terephthalate and polyethylene.The infrared characteristic peaks of each sample in each category were different.For the largest number of samples,the spectral data dimension was reduced,and the principal components were extracted by the principal component analysis.The samples were further grouped by a K-means clustering method.Finally,the clustering results were used as dependent variables to construct a neural network algorithm to train the data to predict the classification of samples.With the help of random number generator,86.5% of the samples were selected as the training set and 13.5% of the samples were selected as the test set.The results indicated that the accuracy of the training set and test set reached 100%.Meanwhile,the accuracy of the K-means clustering results was verified,and a fast classification model for plastic beverage bottles was established.This classification model has good operability with accurate and reliable results.This work provides a reference for the police grassroots to handle cases.

关 键 词：光谱学 傅里叶变换红外光谱 塑料饮料瓶 化学计量学 人工神经网络 

分 类 号：TQ323.41[化学工程—合成树脂塑料工业]