Corrigendum to: Predictive Modeling for Degree of Substitution of Cellulose Acetate using Infrared Spectroscopy and Machine Learning

doi:10.7584/JKTAPPI.2023.12.55.6.150

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2023 Vol.55, Issue 6 Preview Page

Corrigendum

Corrigendum to: Predictive Modeling for Degree of Substitution of Cellulose Acetate using Infrared Spectroscopy and Machine Learning Corrigendum to: 적외선 분광법과 기계학습을 이용한 셀룰로오스 아세테이트의 치환도 예측 모델링: 이용주¹, 이지은², 권재경³, 이태주^3†, 김형진^4‡
Yong Ju Lee¹, Ji Eun Lee², Jae Gyoung Gwon³, Tai Ju Lee^3†, Hyoung Jin Kim^4‡; ¹Department of Forest Products and Biotechnology, Kookmin University, Graduate Student
²국립산림과학원 임산자원이용연구부 임산소재연구과, 연구원
³국립산림과학원 임산자원이용연구부 임산소재연구과, 연구사
⁴국민대학교 과학기술대학 임산생명공학과, 교수

¹Department of Forest Products and Biotechnology, Kookmin University, Graduate Student
²Division of Forest Industrial Materials, Department of Forest Products and Industry, National Institute of Forest Science
³Division of Forest Industrial Materials, Department of Forest Products and Industry, National Institute of Forest Science
⁴Department of Forest Products and Biotechnology, Kookmin University, Professor
^†Corresponding author: E-Mail: leetj@korea.kr (Address: Department of Forest Products and Industry, Division of Forest Industrial Materials, National Institute of Forest Science, Seoul, 02455, Republic of Korea)
^‡Co-corresponding Author: E-Mail: hyjikim@kookmin.ac.kr (Address: Department of Forest Products & Biotechnology, College of Science and Technology, Kookmin University, Seoul, Republic of Korea)

30 December 2023. pp. 150-151

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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 :6
Pages :150-151
The Original Article was published on 30 October 2023

Corrigendum to : Journal of Korea TAPPI(2023) 55(5):83-95 https://doi.org/10.7584/JKTAPPI.2023.10.55.5.83

펄프․종이기술 55(5):83-95 ‘적외선 분광법과 기계학습을 이용한 셀룰로오스 아세테이트의 치환도 예측 모델링 (Lee et al. 2023)’의 본문 중 Fig. 1과 Fig. 2에 인용문헌 표기가 필요하다고 판단되어 해당사항을 다음과 같이 공지함.

Fig. 1. Visualization of machine learning model (a: PLS-DA, b: SVM, c: KNN) (inspired Jang et al. 2020).

Fig. 2. Visualization of confusion matrix for classification with N classes (Abraham et al. 2019).

1. Jang, K. J., Heo, T. Y., and Jeong, S. H., Classification option for Korean traditional paper based on type of raw materials, using near-infrared spectroscopy and multivariate statistical methods, BioResources 15(4):9045-9058 (2020).
2. Abraham, S., Huynh, C., and Vu, H., Classification of soils into hydrologic groups using machine learning, Data 5(1):2 (2019).
DOI :https://doi.org/10.7584/JKTAPPI.2023.12.55.6.150

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

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