Raman spectroscopy and machine learning for forensic document examination
Yong Ju Lee, Chang Woo Jeong, H. Kim, Hong Taek Kim, Tai-Ju Lee, Hyoung Jin Kim, Hyoung Jin Kim · The Analyst · 2025
was identified as a highly informative region for differentiation, reducing the number of input variables from 756 to 360 while enhancing the model accuracy. The FNN model outperformed the RF and SVM models, with an F1 score of 0.968. The results underscore the potential of combining Raman spectroscopy with machine learning for forensic document examination, offering an interpretable, computationally efficient, and robust approach for paper classification.