Classification Analysis of Copy Papers Using Infrared Spectroscopy and Machine Learning Modeling

Abstract

The evaluation and classification of chemical properties in different copy-paper products could significantly help address document forgery. This study analyzes the feasibility of utilizing infrared spectroscopy in conjunction with machine learning algorithms for classifying copy-paper products. A dataset comprising 140 infrared spectra of copy-paper samples was collected. The classification models employed in this study include partial least squares-discriminant analysis, support vector machine, and K-nearest neighbors. The key findings indicate that a classification model based on the use of attenuated-total-reflection infrared spectroscopy demonstrated good performance, highlighting its potential as a valuable tool in accurately classifying paper products and ensuring assisting in solving criminal cases involving document forgery.

Keywords

• attenuated-total-reflection infrared spectroscopy (atr-ir)
• partial least squares-discriminant analysis (pls-da)
• support vector machine (svm)
• k-nearest neighbor (knn)
• machine learning
• document forgery
• forensic document analysis