Nondestructive Identification of Paper Based on Relative Formation Time Using Three-Dimensional Fluorescence Spectroscopy Combined With Supervised Learning

Abstract

In order to achieve nondestructive analysis and identification of the relative formation time of paper evidence and to solve the difficulties in document authenticity identification in the field of forensic science, this study selected three-dimensional fluorescence spectroscopy data of paper evidence of the same brand and model collected in the same storage environment within the last decade (2012–2023). After preprocessing steps like eliminating scattering, smoothing noise and principal component analysis (PCA), machine learning algorithms such as K-nearest neighbor (KNN) and linear discriminant analysis (LDA) were employed to classify and predict specific feature bands. The accuracy of KNN and LDA was 94.5% and 98.9%, respectively. Furthermore, relative formation time prediction was conducted for paper samples by LDA in the sample library, achieving an accuracy rate of 98.0%. Finally, the established model was successfully applied to analyze an actual case involving suspected “forged official documents.” It accurately determined the relative formation time of the forged paper, and the analysis results were consistent with the suspect's confession.