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Spectral Preprocessing and Machine Learning Modeling for Discriminating Manufacturing Origins of Mulberry Bast Fiber
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닥나무 인피섬유의 제조 지역 식별을 위한 적외선 스펙트럼 데이터 전처리 및 기계학습 모델링

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Type
Academic journal
Author
Yong Ju Lee (국민대학교) Soon Wan Kweon (국민대학교) Jae Hyeop Kim (국민대학교) Ji Eun Cha (국민대학교) Kwang-Ho Kang (에이치피프린팅코리아) Hyoung Jin Kim (국민대학교)
Journal
Korea Technical Association Of The Pulp And Paper Industry Journal of Korea Technical Association of the Pulp and Paper Industry Vol.55 No.5(Wn.214) KCI Accredited Journals SCOPUS
Published
2023.10
Pages
61 - 74 (14page)

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Spectral Preprocessing and Machine Learning Modeling for Discriminating Manufacturing Origins of Mulberry Bast Fiber
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The objective of this study was exploring the impact of spectral data preprocessing techniques on the performance of machine learning models for classifying the origin of mulberry bast fibers. The findings indicated that a selective spectral region (1800-1200 cm-1) significantly improves classification model performance. Among the classifiers tested, Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machines (SVM) demonstrated the highest accuracy. Additionally, A spectral preprocessing with the Norris-Williams algorithm effectively improved model performance within the same classifier for this dataset. These results suggest that applying machine learning modeling with spectral preprocessing can enable the origin classification of mulberry bast fibers and provide a chemical basis for classification rules beyond simple categorization.
#Spectral pre-processing #Savitzky-golay #Norris-Williams #PLS-DA #SVM #KNN #machine learning #classification #infrared spectroscopy #Hanji #mulberry

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ABSTRACT
1. 서론
2. 재료 및 방법
3. 결과 및 고찰
4. 결론
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