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Study on the Classification of Thermosensitive Paper by XRF Combined with Multivariate Statistical Analysis
Received:June 12, 2020  
DOI：10.11980/j.issn.0254-508X.2020.12.015
Key Words:X-ray fluorescence speetrometry  thermosensitive paper  principal component analysis  cluster analysis
Fund Project:中国人民公安大学2019年度基科费重点项目（2019JKF222）。
Author NameAffiliationPostcode
JIANG Hong People’s Public Security University of China， Beijing， 100038 100038
WANG Xin People’s Public Security University of China， Beijing， 100038 100038
GU Anzhou People’s Public Security University of China， Beijing， 100038 100038
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Abstract:In order to establish an analytical method for rapid detection of thermal paper， 31 samples of different brands and different series of the same brand were determined by X-ray fluorescence spectrometry （XRF）， and the experimental results were analyzed by principal component analysis and cluster analysis combined with multivariate statistics. According to the kinds and contents of elements in the samples， 31 samples of thermal paper could be effectively distinguished. When the merging distance was the smallest， the samples could be divided into 6 types. Discriminant analysis was performed at the sametime of verification， and the overall prediction accuracy rate was 95.7%.This method did not damage the inspection materials and had good reproducibility， which could provide help for the public security sector in handling cases.
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