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中国无机分析化学:2024,14(6):836-841
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基于高光谱成像的光敏印油种类区分实验
付 沛, 崔 岚, 李 硕
(中国刑事警察学院 刑事科学技术学院)
Experimental research on species differentiation of photosensitive Printing Oil based on Hyperspectral Imaging
FU PEI, CUI LAN, LI SHUO
(College of Criminal Science and Technology, China Criminal Police University)
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投稿时间:2024-01-04    修订日期:2024-01-15
中文摘要: 印油种类区分是法庭科学文件检验领域的重要一环,为研究无损高效区分光敏印油种类的方法。以33种不同品牌光敏印油的原始光谱数据当作对照组,对原始数据进行t-SNE降维和UMAP降维后,选择XGBoost、SVM和MLP三种分类算法,以1比4的比例确定测试集和训练集,对原始数据和降维后的数据进行分类,同时使用网格搜索和五倍交叉验证来优化模型的性能和泛化能力。结果表明,上述三种分类算法对降维后光谱数据区分的平均准确率高于对原始光谱数据区分的平均准确率,且UMAP-MLP分类模型的区分准确率最高,可达到98%。提出的分类模型可用于光敏印油种类的快速区分。
Abstract:The classification of printing oil is an important part of the document examination, in order to research the method of non-destructive and efficient identification of photosensitive printing oil. Taking the original spectral data of 33 different brands of photosensitive printing oil as the control group, after the original data were reduced by t-SNE and UMAP, three classification algorithms, XGBoost, SVM and MLP, were selected to determine the test set and training set in the ratio of 1: 4, and the original data and the reduced data were classified. At the same time, grid search and quintuple cross validation were used to optimize the performance and generalization ability of the model. The results show that the average accuracy of the above three classification algorithms for reduced-dimensional spectral data is higher than that of the original spectral data, and the UMAP-MLP classification model has the highest accuracy of 98%. The proposed classification model can be used to quickly distinguish the types of photosensitive printing oil.
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基金项目:公安部科技强警基础工作计划(2022JC03)、“十三五”国家重点研发计划项目 (2016YFC0800705)
引用文本:
付 沛,崔 岚,李 硕.基于高光谱成像的光敏印油种类区分实验[J].中国无机分析化学,2024,14(6):836-841.
FU PEI,CUI LAN,LI SHUO.Experimental research on species differentiation of photosensitive Printing Oil based on Hyperspectral Imaging[J].Chinese Journal of Inorganic Analytical Chemistry,2024,14(6):836-841.

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