分析测试学报2024，Vol.43Issue(4)：523-531,9.DOI:10.12452/j.fxcsxb.23111401
高光谱喷墨打印墨水数据的非线性降维及分类建模方法研究
Research on Nonlinear Dimensionality Reduction and Classification Modeling Methods of Hyperspectral Inkjet Printing Ink Data
摘要
Abstract
In the practice of forensic science,it is often necessary to accurately determine the identi-ty of the test material and the sample document by analyzing the composition of the ink in the docu-ment.Hyperspectral imaging technology combined with machine learning was used to distinguish the types of inkjet printing inks.Hyperspectral images of documents printed with 4 colors(black,blue,magenta and yellow)of 14 sets of different brands and models were collected in the range of 400-1 000 nm,and spectral data of 56 samples were extracted.Use the uniform manifold approximation and pro-jection(UMAP)and T-distributed stochastic neighbor embedding(t-SNE)two algorithms for hyper-spectral data dimension reduction processing inkjet printing ink,and then establish extreme gradient boosting(XGBoost),light gradient boosting machine(LightGBM)and support vector machine(SVM),determine the test set and training set in the ratio of 1∶4,and classify the original data and the data after dimensionality reduction respectively.The experimental results show that UMAP dimension re-duction algorithm combined with SVM model has the best effect on the classification of inkjet printing inks.The classification accuracy of black ink samples is about 90%,and the classification accuracy of other color ink samples is 100%.This study provides a new,non-destructive and accurate identifi-cation method for inkjet printing documents.关键词
高光谱成像技术/喷墨打印墨水/降维算法/分类模型Key words
hyperspectral imaging technology/inkjet printing ink/dimensionality reduction algo-rithm/classification model分类
化学化工引用本文复制引用
李硕,崔岚,付沛..高光谱喷墨打印墨水数据的非线性降维及分类建模方法研究[J].分析测试学报,2024,43(4):523-531,9.基金项目
公安部科技强警基础工作计划(2022JC03) （2022JC03）
"十三五"国家重点研发计划项目资助(2016YFC0800705) （2016YFC0800705）
中国刑事警察学院研究生创新能力提升项目(2023YCZD07) （2023YCZD07）
