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| Differential Raman Spectroscopy Combined with PCA-RCSC and Improved Transformer for Courier Face Sheets Inspection Research |
| Received:April 03, 2025 Revised:June 27, 2025 |
| DOI：10.11980/j.issn.0254-508X.2025.11.023 |
| Key Words:Differential Raman spectroscopy courier face sheets orthogonally constrained principal component analysis elemental ratio-cosine similarity clustering |
| Fund Project:安徽公安学院校级科研项目（2024xjkyyb08）。 |
| Author Name | Affiliation | Postcode | | JIANG Hong* | Department of Criminal Science and Technology, Hu’nan Police College, Changsha, Hu’nan Province, 410138
College of Investigation, People’s Public Security University of China, Beijing, 100038
Center of Forensic Science of Beijing Hui Zheng Zhuo Yue Technology Co., Ltd., Beijing, 102446 | 102446 | | MA Xingyu | College of Investigation, People’s Public Security University of China, Beijing, 100038 | 100038 |
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| Abstract:Addressing the challenges of easily fading handwriting and stable filler components in thermal paper-based courier face sheets， this study collected data from 173 express delivery label samples from various brands and printing dates through differential Raman spectroscopy， and proposed a novel method integrating modified orthogonally constrained principal component analysis （PCA）， and element ratio-cosine similarity clustering (RCSC)， combined with a Transformer model incorporating a sparse attention mechanism for data classification prediction. The results showed that orthogonally constrained PCA reduced the dimension of differential Raman spectral data and resulted in a compression rate of 95.6%， while RCSC supplemented by manual validation， categorized the samples into four classes. Further classification using the sparse attention-based Transformer model achieved an forecast accuracy of 90.0%， significantly outperforming traditional methods such as random forest and support vector machines. |
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