Identification of Pulp Fibers in the Chronological Academic Journals using Spectroscopic Method

Kim, Kang-Jae; Ahn, Eun-Byeol; Ryu, Ji-Ae; Eom, Tae-Jin

doi:10.7584/jktappi.2018.08.50.4.61

Cited by 5 publications

(1 citation statement)

References 19 publications

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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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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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Comparison of 3D Printer Application and Strength Property Using Polylactic Acid Filaments with Lignin-free and -rich MFC

Ryu¹,

Lee²,

Eom³

2019

J. of Korea TAPPI

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Predictive Modeling of Korean Traditional Paper Characteristics Using Machine Learning Approaches (Part 2): Prediction of Carbonyl Content and Analysis of Variable Importance Using Random Forest

Kim,

Park

et al. 2023

J. of Korea TAPPI

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This paper introduces a random forest regression model trained with infrared spectral data to predict the carbonyl content of Hanji, a traditional Korean paper. The random forest model demonstrated excellent performance in carbonyl content prediction, surpassing the results obtained from the partial least squares model. To optimize the infrared spectral range for prediction, the spectral range was restricted from the entire range of 4000-400 cm -1 to the narrower range of 1800-1200 cm -1 , known for its suitability in characterizing paper properties. This limitation enhanced the coefficients of determination of the model, increasing it from 0.921 to 0.937. A permutation variable importance measure was then applied to identify the key spectral regions contributing to carbonyl content prediction. The analysis pinpointed the 1650-1350 cm -1 range as a crucial region for accurate predictions. Subsequently, a new prediction model was built using data exclusively from this important region, yielding remarkably improved coefficients of determination of 0.960 and 0.965 for the raw and second derivative spectra, respectively. These findings affirm the validity and significance of the critical region identified by the permutation variable importance

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Identification of Pulp Fibers in the Chronological Academic Journals using Spectroscopic Method

Cited by 5 publications

References 19 publications

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

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

Comparison of 3D Printer Application and Strength Property Using Polylactic Acid Filaments with Lignin-free and -rich MFC

Predictive Modeling of Korean Traditional Paper Characteristics Using Machine Learning Approaches (Part 2): Prediction of Carbonyl Content and Analysis of Variable Importance Using Random Forest

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