A Study on the Classification of European Copy Papers with Infrared Spectroscopy and Principal Component Analysis

Kim, Jin-Ho; Kang, Kwang-Ho; Kim, Kang-Jae

doi:10.7584/jktappi.2022.08.54.4.34

Cited by 3 publications

(6 citation statements)

References 12 publications

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“…Kim et al (2016) utilized IR spectroscopy and PCA to effectively distinguish traditional paper products originating from Korea, China, and Japan. Similarly, Kang et al (2021) and Kim et al (2022) demonstrated the efficacy of IR and PCA in classifying paper products according to their respective continents or countries of origin.…”

Section: Introductionmentioning

confidence: 91%

“…If the papers contain sizing agents or inorganic fillers on their surface, they may demonstrate a relatively higher crystallinity index (Kang et al 2021;Kim et al 2022). The TCI in Fig.…”

Section: Atr-ir Datamentioning

confidence: 99%

“…In recent years, the utilization of infrared spectroscopy has gained prominence for identifying paper types (Kher et al 2001(Kher et al , 2005Kumar et al 2017;Kang et al 2021;Kim et al 2022). Infrared spectroscopy (IR) serves as a fundamental tool for investigating paper structure and pulp chemistry (Workman 1999;Pan and Nguyen 2007).…”

Section: Introductionmentioning

confidence: 99%

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Classification analysis of copy papers using infrared spectroscopy and machine learning modeling

Lee,

Kim

2023

BioRes

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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.

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Section: Introductionmentioning

confidence: 91%

“…If the papers contain sizing agents or inorganic fillers on their surface, they may demonstrate a relatively higher crystallinity index (Kang et al 2021;Kim et al 2022). The TCI in Fig.…”

Section: Atr-ir Datamentioning

confidence: 99%

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Classification analysis of copy papers using infrared spectroscopy and machine learning modeling

Lee,

Kim

2023

BioRes

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“…Choi et al [11,12] used SEM-EDS to analyze fillers for 188 types of copy paper and further determined their mix ratios of fiber components by dissociation tests. Kim et al [13,14] reported that it was possible to identify three types of the copy paper and four types of Korean, Chinese, and Japanese traditional paper by performing principal component analysis (PCA) of their IR/NIR spectra. They also reported that six types of copy paper made in Europe could be identified using FT-IR spectrometry with PCA.…”

Section: Previous Workmentioning

confidence: 99%

Identification of document paper using hybrid feature extraction

Lee

Kim

Yook

et al. 2023

Journal of Forensic Sciences

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Document forgery is a significant issue in Korea, with around ten thousand cases reported every year. Analyzing paper plays a crucial role in examining questionable documents such as marketable securities and contracts, which can aid in solving criminal cases of document forgery. Paper analysis can also provide essential insights in other types of criminal cases, serving as an important clue for solving cases such as the source of a blackmail letter. The papermaking process generates distinct forming fabric marks and formations, which are critical features for paper classification. These characteristics are observable under transmitted light and are created by the forming fabric pattern and the distribution of pulp fibers, respectively. In this study, we propose a novel approach for paper identification based on hybrid features. This method combines texture features extracted from images converted using the gray‐level co‐occurrence matrix (GLCM) approach and a convolutional neural network (CNN), with another set of features extracted by the CNN using the same images as input. We applied the proposed method to classification tasks for seven major paper brands available in the Korean market, achieving an accuracy of 97.66%. The results confirm the applicability of this method for visually inspecting paper products and demonstrate its potential for assisting in solving criminal cases involving document forgery.

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“…[3][4][5] 종이를 비롯한 목질 계 소재의 정보 획득과 분석에서도 분광법은 매우 유용 하며, 분석 도구로는 주로 통계적 다변량 분석 기법이 활 용되고 있다. [6][7][8][9][10] Kang 등(2021) 11) 과 Kim 등(2022) 12) 은 IR 분광법과 주성분 분석을 이용하여 복사지의 분류 가능성을 확인하였으며, Kim과 Eom(2016) 13) 은 지종 분류에 대한 IR과 NIR 분광법의 차이를 조사하였다. 이 러한 접근 방식은 종이의 열화 특성 분석에서도 유효함 이 확인되었다.…”

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Predictive Modeling of Traditional Korean Paper Characteristics Using Machine Learning Approaches (Part 1): Discriminating Manufacturing Origins with Artificial Neural Networks and Infrared Spectroscopy

Hwang,

Park,

Kim

et al. 2023

J. of Korea TAPPI

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This study focuses on machine learning-based approaches in combination with infrared spectroscopy to discriminate the manufacturing origin of Hanji, a traditional Korean paper. Infrared spectra provide useful information about the chemical composition and structural features of Hanji, while principal component analysis and hierarchical clustering extract meaningful patterns related to the manufacturing region. Score plots and hierarchical clustering of the principal components provide enhanced clustering patterns based on manufacturing region by focusing on the spectral region 1800-1200 cm -1 . The clustering patterns are driven by key absorption bands, such as those associated with carboxyl groups, crystalline cellulose, and aromatic rings. In addition, feed-forward neural network classification models that were developed using the spectral data exhibit significant accuracy when classifying the Hanji manufacturing regions. In particular, models utilizing the raw and second derivative spectra in the 1800-1200 cm -1 region exhibit excellent classification performance, indicating the effectiveness of this spectral region for classification purposes. This study demonstrates

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A Study on the Classification of European Copy Papers with Infrared Spectroscopy and Principal Component Analysis

Cited by 3 publications

References 12 publications

Classification analysis of copy papers using infrared spectroscopy and machine learning modeling

Classification analysis of copy papers using infrared spectroscopy and machine learning modeling

Identification of document paper using hybrid feature extraction

Predictive Modeling of Traditional Korean Paper Characteristics Using Machine Learning Approaches (Part 1): Discriminating Manufacturing Origins with Artificial Neural Networks and Infrared Spectroscopy

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