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