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2023 Vol.55, Issue 6 Preview Page

Research Article

Identification of Mulberry Bast Fiber Using a Multivariate Analysis Technique

다변량 분석 기법을 활용한 닥나무 인피섬유의 식별에 관한 연구

Soon Wan Kweon1
,
Yong Ju Lee1
,
Na Young Kang1
,
A Yeon Lee1
,
Hyoung Jin Kim2†
권 순완1
,
이 용주1
,
강 나영1
,
이 아연1
,
김 형진2†
1Dept. of Forest Products & Biotechnology, College of Science and Technology, Kookmin Univ.
2Dept. of Forest Products & Biotechnology, College of Science and Technology, Kookmin Univ.
1국민대학교 과학기술대학 임산생명공학과, 학생
2국민대학교 과학기술대학 임산생명공학과, 교수
†Corresponding author: E-Mail: hyjikim@kookmin.ac.kr (Address: Dept. of Forest Products & Biotechnology, College of Science and Technology, Kookmin Univ. Seoul, Republic of Korea)
30 December 2023. pp. 59-69
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Abstract

Mulberry bast fiber, the primary raw material in Korean paper, exhibits superior crystallinity and polymerization of cellulose compared with wood fiber, contributing to the exceptional tensile strength and durability of Hanji, a traditional Korean paper made from mulberry paper. Therefore, Hanji is robust and has excellent preservation qualities. The expansion of the domestic conservation science industry and the efficient management of mulberry fiber supply and demand necessitate the adoption of non-destructive methods for analysis. This study proposes a taxonomic strategy for identifying the origins of paper mulberry bast fiber through non-destructive methods, paving the way for broader applications of this fiber. The mulberry bast fiber samples were categorized based on their origins and pulping methods. Infrared spectroscopy, coupled with unsupervised learning algorithms, including principal component analysis (PCA), multidimensional scaling (MDS), and K-means cluster analysis (KMCA), was employed for multivariate analysis. The findings demonstrate the accurate classification based on pulping methods. However, classification by country of origin was only partially successful. These outcomes suggest the potential for non-destructive analysis of national records using traditional Korean paper, contingent upon effective application of this algorithm.

Keywords
Hanji
mulberry
infrared spectroscopy
multivariate analysis
PCA
MDS
KMCA
classification
unsupervised learning
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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 :59-69
  • Received Date : 2023-10-10
  • Revised Date : 2023-12-07
  • Accepted Date : 2023-12-10
  • DOI :https://doi.org/10.7584/JKTAPPI.2023.12.55.6.59
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