Classification Analysis of Copy Papers Using Infrared Spectroscopy and Machine Learning Modeling - Machine Learning for Forensic Document Analysis

Aug 4, 2026

Evaluation and classification of chemical properties across copy-paper products support document forgery detection. This study assesses the feasibility of combining infrared spectroscopy with machine-learning algorithms to classify copy-paper types. A dataset of 140 ATR-IR spectra is used to train models including PLS-DA, SVM, and KNN. Results show strong performance for attenuated-total-reflection infrared spectroscopy, indicating reliable paper-product classification to aid forensic investigations involving document forgery.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Introduction

Copy-paper properties and use cases

Document forgery and forensic needs

Prior classification methods for paper materials

Infrared spectroscopy for paper identification

Multivariate methods and machine learning models

Classification Analysis of Copy Papers Using Infrared Spectroscopy and Machine Learning Modeling Yong-Ju Lee,a Tai-Ju Lee,b, * and Hyoung Jin Kim a, * 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. DOI: 10.15376/biores.19.1.160-182 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 Contact information: a: Department of Forest Products and Biotechnology, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707 Republic of Korea; b: National Institute of Forest Science, Department of Forest Products and Industry, Division of Forest Industrial Materials, 02455, Seoul, Republic of Korea; *Corresponding authors: [leetj@korea.kr](leetj@korea.kr); [hyjikim@kookmin.ac.kr](hyjikim@kookmin.ac.kr) INTRODUCTION Various types of copy papers are produced and sold worldwide, finding extensive usage in institutions such as schools, offices, and printing companies. Each copy paper possesses distinct properties, including basis weight, whiteness, and composition. These attributes are influenced by factors, such as the type and ratio of pulp; additives, such as fillers and sizing agents; and variations in manufacturing processes. Surface sizing agents, such as starch, are commonly employed in copy-paper manufacturing to enhance surface strength and printing suitability (Moutinho et al. 2011) . Despite the advances in information technology that have led to the concept of apaperless society, many tasks are still conducted using paper documents. Paper remains a crucial medium for recording various daily tasks and activities, including taking notes, jotting down memos, and formalizing important contracts (Ganzerla et al. 2009; Lee et al. 2023) . Meanwhile, owing to recent technological advances in office automation devices, document forgery has become more accessible, consequently underscoring the growing significance of paper document examination in fields such as forensic investigation and evidence analysis (Lee et al. 2023) . In South Korea, a substantial number of document forgery cases have been documented. In 2020, the country recorded 2,217 cases of document forgery related to official documents and 7,604 cases concerning private documents. Over a three-year period starting in 2018, the statistics indicate that nearly ten thousand criminal cases of document forgery are reported annually (Choi et al. 2018a; 2018b) . Hence, the development of robust forensic capabilities for detecting forgery becomes paramount (Kim et al. 2016; Lee et al. 2023) . Various classification methods for paper have been reported, primarily relying on chemical analysis. The X-ray diffraction (XRD) method has been employed to investigate the inorganic substances used as paper fillers (Foner and Adan 1983; Causin et al. 2010) . Spence et al. (2000) demonstrated that document paper could be identified by utilizing ICP-MS to trace the elements within the paper. Ganzerla et al. (2009) conducted a comprehensive study on the characteristics of ancient

FAQs

What is the main goal of the study on copy-paper classification?

To evaluate chemical-property classification of copy-paper products using infrared spectroscopy together with machine-learning models for forensic document analysis.

What data was collected and how many spectra are included?

The study collected a dataset of 140 infrared spectra from copy-paper samples for model training and evaluation.

Which machine-learning models are used to classify the paper products?

The classification models include partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), and K-nearest neighbors (KNN).

Why is attenuated-total-reflection infrared spectroscopy important in the results?

A model based on attenuated-total-reflection infrared spectroscopy achieved good performance, supporting accurate classification of paper products for assisting forgery investigations.

Aug 4, 2026

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