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OA北大核心CSTPCD

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.