Nondestructive and Rapid Identification of Stamp Pad Ink Based on Hyperspectral Imaging and Extreme Learning Machine

Abstract

The examination of stamp pad ink in questioned documents serves as a crucial scientific basis for forensic authentication. This study presents a novel rapid classification framework integrating Hyperspectral Imaging (HSI) and Extreme Learning Machine (ELM) to address the challenges of timeliness and accuracy in nondestructive ink detection. A total of 24 photosensitive ink samples from 21 brands were collected, generating 72 standardized stamped impressions. Spectral-spatial data were acquired using an HSI system (400-1000 nm, 5 nm spectral resolution). preprocessed by Multiplicative Scatter Correction (MSC) to mitigate substrate interference. Experimental results demonstrate that the HSI-MSC-ELM framework achieved an accuracy of 98.38% on the test set without feature dimensionality reduction (full 121 spectral bands), outperforming Random Forest (RF) by 4.63% and Backpropagation Neural Network (BPNN) by 6.34%. Crucially, the detection time was only 1.59 seconds – 28× faster than RF (45.90 s) and 285× faster than BPNN (453.36 s). This approach provides a simple, nondestructive, and efficient solution for forensic document examination, with potential to replace traditional destructive techniques.

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Article information

Article type
Paper
Submitted
01 Apr 2025
Accepted
30 Jun 2025
First published
02 Jul 2025

Anal. Methods, 2025, Accepted Manuscript

Nondestructive and Rapid Identification of Stamp Pad Ink Based on Hyperspectral Imaging and Extreme Learning Machine

X. Lu, J. Zhang, J. Wu, X. Zhang, H. Ren, H. Chen, K. Ma and F. Li, Anal. Methods, 2025, Accepted Manuscript , DOI: 10.1039/D5AY00526D

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