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一次性塑料包装瓶盖(杯盖)的拉曼光谱分析
引用本文:田陆川,石钰,姜红,张斯昱,陈敏璠.一次性塑料包装瓶盖(杯盖)的拉曼光谱分析[J].上海塑料,2022(1).
作者姓名:田陆川  石钰  姜红  张斯昱  陈敏璠
作者单位:中国人民公安大学侦查学院;北京鉴知技术有限公司
基金项目:中国人民公安大学2021年度基科费重点项目(2021JKF212);国家重点研发计划项目(2017YFC0822004)。
摘    要:利用便携式拉曼光谱仪对收集到49个现售饮品或外卖配送饮品的一次性塑料杯盖样品和饮料瓶瓶盖样品进行检验分析,先根据样品外观进行分类,再根据拉曼位移按照成分进行分组,最后再通过计算相对峰高比进行进一步区分,都取得了较好的效果。建立了基于系统聚类的分类模型,利用主成分分析对60%的样本进行了降维和分类。最终被检样本被分为6类,分类效果较好。建立了一种快速无损检验一次性塑料包装瓶盖的方法,通过化学计量学和传统谱图解析方式可以使不同组间、同组不同样品间都可获得区分,可以为公安实际办案提供新的思路与参考。

关 键 词:拉曼光谱  一次性塑料包装  检验区分  主成分分析  系统聚类

Raman Spectrum Analysis of Disposable Plastic Packaging Bottle Caps(Cup Caps)
TIAN Luchuan,SHI Yu,JIANG Hong,ZHANG Siyu,CHEN Min.Raman Spectrum Analysis of Disposable Plastic Packaging Bottle Caps(Cup Caps)[J].Shanghai Plastics,2022(1).
Authors:TIAN Luchuan  SHI Yu  JIANG Hong  ZHANG Siyu  CHEN Min
Affiliation:(Detective College,People’s Public Security University of China,Beijing 100038,China;JINSP Co.,Ltd.,Beijing 100084,China)
Abstract:The portable Raman spectrometer was used to inspect and analyze 49 disposable plastic cup cap samples and beverage bottle cap samples collected from ready-made drinks or take away drinks.Firstly,they were classified according to the appearance of the samples,then grouped according to the components according to the Raman displacement,and finally further distinguished by calculating the relative peak height ratio,which had achieved good results.A classification model based on systematic clustering was established,and 60%of the samples were classified by principal component analysis.The final samples were divided into 6 categories,and the classification effect was good.A rapid nondestructive method for testing disposable plastic packaging bottle caps is established.Through chemometrics and traditional spectrum analysis,different groups and different products in the same group can be distinguished,which can provide new ideas and references for the actual handling of public security cases.
Keywords:Raman spectrum  disposable plastic packaging  inspection and differentiation  principal component analysis  systematic clustering
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