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

Sung-Wook Hwang; Geunyong Park; Jinho Kim; Myung-Joon Jeong

doi:10.7584/JKTAPPI.2023.8.55.4.57

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2023 Vol.55, Issue 4 Preview Page

Research Article

Predictive Modeling of Traditional Korean Paper Characteristics Using Machine Learning Approaches (Part 1): Discriminating Manufacturing Origins with Artificial Neural Networks and Infrared Spectroscopy 기계학습을 이용한 한지의 특성 예측 모델링 (Part 1) -인공신경망과 적외선 분광법을 이용한 제조 지역 판별-

30 August 2023. pp. 57-69

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Abstract

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 the effective application of artificial neural networks in conjunction with infrared spectroscopy to characterize and classify Hanji based on its manufacturing region. The results contribute to a better understanding of the unique properties of Hanji and the discovery of new insights from paper cultural artifacts.

Keywords

Artificial neural networks

classification

Hanji

hierarchical clustering

infrared spectroscopy

principal component analysis

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Information

Publisher :Korea Technical Association of The Pulp and Paper Industry
Publisher(Ko) :한국펄프종이공학회
Journal Title :Journal of Korea TAPPI
Journal Title(Ko) :펄프종이기술
Volume : 55
No :4
Pages :57-69
Received Date : 2023-07-10
Revised Date : 2023-08-14
Accepted Date : 2023-08-16
DOI :https://doi.org/10.7584/JKTAPPI.2023.8.55.4.57

Journal of Korea TAPPI ISSN:0253-3200(Print) 펄프종이기술

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