|
|
|
|
|
|
| Feature Analysis and Classification of the Line-Crossing Sequences
Between Stamp Inks and Laser Printing Toner Based on
Hyperspectral Techniques |
| LI Chang-sheng, GAO Shu-hui*, LI Kai-kai |
School of Investigation, People's Public Security University of China, Beijing 100038, China
|
|
|
|
|
Abstract Determining line-crossing sequences between stamp inks and laser printing toner forms a core component of questioned document examination, offering critical evidence for authenticating questioned documents. Current methodologies are hindered by examiner dependency and limited applicability, failing to meet the demands for non-destructive, automated, and high-precision detection. While spectrochemical techniques combined with pattern recognition show promise in non-destructive analysis, they remain insufficient for investigating ink-toner interpenetration cases and model accuracy. This study proposes a novel approach that integrates visible-near-infrared hyperspectral imaging (Vis-NIR HSI) with machine learning, utilizing 10 000-pixel spectra per sample from three ink types and laser printers. Initially, a systematic multi-angle spectral difference calculation strategy was employed to quantify the temporal characteristics of material deposition sequences accurately. Following this, a new method that combines standard normal variate (SNV) spectral preprocessing with the interval variable iterative space shrinkage algorithm (iVISSA) was used to enhance model performance by minimizing noise and intelligently selecting wavelengths. In conclusion, a comprehensive comparative analysis of six machine learning models, including logistic regression (LR), support vector machines (SVM), and random forests (RF), was conducted to develop a high-accuracy classification system for questioned document examination. Experimental results demonstrate that the log ratio method effectively improves spectral differentiation. The combined application of SNV preprocessing and iVISSA-based characteristic wavelength selection successfully reduces data redundancy while significantly boosting model performance. Among the evaluated algorithms, SVM, XGBoost, and LightGBM emerged as the top models, showing robust capability in determining line-crossing sequences between compatible stamp inks and laser printing toners. Validation confirms the method's superior generalization ability for questioned document examination. This study addresses limitations by demonstrating the feasibility of integrating Vis-NIR HSI with pattern recognition to analyze line-crossing sequences of compatible stamp inks. This approach enhances conventional techniques, adding substantial judicial value in fighting document fraud.
|
|
Received: 2025-01-14
Accepted: 2025-06-08
|
|
|
|
Corresponding Authors:
GAO Shu-hui
E-mail: gaoshuhui@ppsuc.edu.cn
|
|
[1] Li B. Journal of Forensic Sciences, 2016, 61(3): 809.
[2] Ministry of Public Security, the People's Republic of China(中华人民共和国公安部). GA/T 1958—2021 Forensic Sciences—Specifications for Examination of the Sequence Between Stamps and Strokes(GA/T 1958—2021 法庭科学朱墨时序检验规范), 2021.
[3] TAO Ke-ming, YAN Yu-hua(陶克明, 阎育华). Forensic Science and Technology(刑事技术), 1989, (4): 13.
[4] Lee K Y, Lee J, Kong S G, et al. Forensic Science International, 2014, 234: 120.
[5] OUYANG Guo-liang(欧阳国亮). Journal of Henan Judicial Police Vocational College(河南司法警官职业学院学报), 2020, 18(2): 102.
[6] LI Biao, FENG Ming-shuai(李 彪, 冯明帅). Guangdong Gongan Keji(广东公安科技), 2012, 20(1): 10.
[7] SUN Hua-qing, DONG Jun, XU Yan-yan, et al(孙华清, 董 军, 许燕燕, 等). China Public Security(Academy Edition)[中国公共安全(学术版)], 2012, (1): 113.
[8] XIE Peng, LI Biao(谢 朋, 李 彪). Journal of People's Public Security University of China(Science and Technology) [中国人民公安大学学报(自然科学版)], 2003, (2): 36.
[9] Wu X, Fang F, Li B. Journal of Forensic Sciences, 2019, 64(6): 1761.
[10] Wu X, Li B, Ouyang G. Journal of Forensic Sciences, 2021, 66(4): 1545.
[11] Kaur R, Kaur G. International Journal of Medical Toxicology and Legal Medicine, 2021, 24(3 and 4): 73.
[12] Meneghetti M, Mancini L, Caligiore G, et al. Forensic Science International, 2024, 360: 112051.
[13] Bojko K, Roux C, Reedy B J. Journal of Forensic Sciences, 2008, 53(6): 1458.
[14] Morais D R de, Campêlo J de M, Razzo D, et al. Analytical Methods, 2020, 12(7): 951.
[15] Barac M, Filko A, Siketic Z, et al. Forensic Science International, 2022, 331: 111136.
[16] Mathayan V, Sortica M, Primetzhofer D. Journal of Forensic Sciences, 2021, 66(4): 1401.
[17] Liu S, Yang Y, Zhang Y, et al. Journal of Forensic Sciences, 2024, 69(6): 2148.
[18] Dirwono W, Park J S, Agustin-Camacho M R, et al. Forensic Science International, 2010, 199(1-3): 6.
[19] Wang Y, Li B. Science & Justice, 2012, 52(2): 112.
[20] Silva C S, Pimentel M F, Honorato R S, et al. Analyst, 2014, 139(20): 5176.
[21] Borba F de S L, Jawhari T, Honorato R S, et al. Analyst, 2017, 142(7): 1106.
[22] Brito L R, Braz A, Honorato R S, et al. Forensic Science International, 2019, 298: 169.
[23] Brito L R, Chaves A B, Braz A, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019, 223: 117287.
[24] Freden S C, Mercanti E P, Becker M A. Third Earth Resources Technology Satellite-1 Symposium: Section a-B. Technical Presentations. Scientific and Technical Information Office, National Aeronautics and Space Administration, 1974.
[25] Chavez Jr P S. Remote Sensing of Environment, 1988, 24(3): 459.
[26] Barnes R J, Dhanoa M S, Lister S J. Applied Spectroscopy, 1989, 43(5): 772.
[27] Deng B-C, Yun Y-H, Ma P, et al. Analyst, 2015, 140(6): 1876.
[28] Li W, Chen C, Su H, et al. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(7): 3681.
[29] Chaurasia V, Pal S. SN Computer Science, 2020, 1(5): 270.
[30] Herath H C M. Performance Evaluation of Machine Learning Classifiers for Hyperspectral Images. 2021 IEEE 21st International Conference on Communication Technology (ICCT). 2021, 1216.
[31] Vanneschi L, Silva S. Decision Tree Learning in: Lectures on Intelligent Systems. Cham: Springer International Publishing, 2023, 149.
[32] Jafarzadeh H, Mahdianpari M, Gill E, et al. Remote Sensing, 2021, 13(21): 4405.
[33] Samat A, Li E, Wang W, et al. Remote Sensing, 2020, 12(12): 1973.
[34] Zheng R, Jia Y, Ullagaddi C, et al. Food Chemistry, 2024, 456: 140062.
[35] XIE Shuo-yue(谢硕玥). Journal of Sichuan Police College(四川警察学院学报), 2018, 30(5): 14.
[36] Bhat S, Mansoor A, Georgescu B, et al. Scientific Reports, 2023, 13(1): 21097. |
| [1] |
ZHANG Meng-fan1, LI Mao-gang1*, LIU Yi-jiang1, YAN Chun-hua1, ZHANG Tian-long2, LI Hua1, 2*. Rapid Quantitative Analysis of Trace Elements in Petroleum Coke by LIBS Combined With Whale Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(10): 2796-2803. |
| [2] |
WU Qiang1, YANG Mo-han1, DUAN Feng-hui1, WANG Zan-pu2, KANG Jia-kun1, YANG Hao3, YANG Gui-jun3, ZHANG Zhi-yong1, MA Xin-ming1, CHENG Jin-peng1*. Accurate Estimation of Maize Above-Ground Biomass Using Integrated Multispectral and LiDAR Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(10): 2906-2914. |
| [3] |
LIU En-qin1, 2, HUANG Wei3, XU Yong3, YANG Man2, GAO Bing2, MO Ding-ru4. Hyperspectral Camouflaged Identification Driven by Spatial-Spectral
Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(10): 2942-2949. |
| [4] |
SHENG Shu-li1, ZOU Ming-min1, 2*, LIU Tian-qi1, CHENG Yong-ping1, CHEN Zi-zheng1, WANG Xu-wen1. Research on Satellite Greenhouse Gas Remote Sensing Retrieval Methods Based on Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(10): 2983-2991. |
| [5] |
ZHANG Xin-zhi1, SAIMAITI Patiguli1, ZHANG Lu-wen1, YANG Ya-fei2*, BIAN Xi-hui1, 3*. Research Progress on Geographical Origin Discrimination of Traditional Chinese Medicines Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(09): 2410-2416. |
| [6] |
WEN Zhu1, GUO Song1, SHU Tian1, ZHAO Long-cai2, 3. Hyperspectral Estimation of Selenium Content in Selenium-Rich Tea Based on Feature Selection and Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(09): 2590-2596. |
| [7] |
KONG Ling-yuan1, 2, HUANG Qing-feng1, 2, NI Chen1, 2, XU Jia-jia1, 2, TANG Xue-hai1, 2*. Estimation of Nitrogen Content of Carya illinoinensis Leaves Based on Canopy Hyperspectral and Wavelet Transform at Different Flight Heights[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(08): 2200-2209. |
| [8] |
WANG Jia-ying1, ZHU Yu-ting1, BAI Hao1, CHEN Ke-ming1, ZHAO Yan-ru1, 2, 3, WU Ting-ting1, 2, 3, MA Guo-ming4, YU Ke-qiang1, 2, 3*. Detecting the Metal Elements and Soil Organic Matter in Farmland by Dual-Modality Spectral Technologies[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(08): 2317-2325. |
| [9] |
WU Qiang1, LU Ling2, FENG Xiao-juan2, WANG Meng2, WANG Yong-long2, HOU Ding-yi3, FAN Bo-bo2*, LIU Jie2*. Hyperspectral Identification and Classification of Different Cultivation Methods of A. mongholicus[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(07): 1842-1847. |
| [10] |
SUN Meng1, CHENG Jun1, DIAO Shu1, HAN Tian-yu2, YU Zhi-long1, LI Jing-wen2, XIE Yun-fei1*. Rapid Determination of Moisture Content of Freeze-Dried Carrots by
Terahertz Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(07): 1924-1931. |
| [11] |
YANG Guang-hui1, 3, ZHANG Yong-li1, 2, 3*, WANG Mei-pan1, 3, LIU Yan-de4, JIANG Xiao-gang4, SUN Jing2, 3, ZHOU Xin-qun2, 3, HAN Tai-lin1*. Determination of Soluble Solids Content in Fresh Corn by Near Infrared
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(07): 1932-1939. |
| [12] |
JIANG Heng1, LÜ Zi-wei1, LI Yang2, DONG Tuo1*. A Novel Strategy for Viral Detection in Acute Respiratory Infections: Combining SERS With Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(05): 1217-1224. |
| [13] |
LI Ruo-tong1, HU Hui-qiang2, CAO Shi-yu1, LU Meng-yao1, LIU Meng-ran1, FU Jia-yue1, MAO Xiao-bo2, WANG Hai-bo3*, FU Ling1, 3*. Identification of Pinelliae Rhizoma Decoction Pieces by Hyperspectral
Imaging Combined With Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(05): 1236-1242. |
| [14] |
DENG Ying-jiao1, CHEN Jun2, WANG Jian-sheng1, HU Liu-ping3, ZHANG Qing1, DU Yu-zhen3, WANG Yan1, LI Qing-li1*. Analysis of Urine Sediment Samples Based on Microscopy Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(05): 1243-1250. |
| [15] |
LI Wei-yan1, TENG Jing2*, ZHENG Zhi-hui3, 4, SHI Jing-jing4, SHI Yao4*, LI Zhi-hong4, ZHANG Chen-mu4. Rapid Classification and Identification of Heavy Metal-Containing
Electroplating Sludge by Combining EDXRF With Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(05): 1283-1289. |
|
|
|
|
