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Classification option for Korean traditional paper based on type of raw materials, using near-infrared spectroscopy and multivariate statistical methods
- Source :
- BioResources. 15:9045-9058
- Publication Year :
- 2020
- Publisher :
- BioResources, 2020.
-
Abstract
- Depending on the different types of raw materials used to produce hanji, a Korean traditional handmade paper, there can be significant differences in the durability and mechanical properties of the final product. In this study, near-infrared spectroscopy (NIR) combined with multivariate statistical methods were used to confirm the classification possibility of hanji based on the various type of raw materials. The hanji papers were prepared from paper mulberry trees, cooking agents, and mucilage. Altogether, a total of 60 hanji spectra were collected by NIR. Then, the 60 spectra were grouped into four categories: the control, paper mulberry, cooking agent, and mucilage type based on each of the types of raw materials contained in the hanji. Three different classification algorithms – partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and random forest (RF) – were used to classify the hanji types. The best hanji material classification performance was obtained when the hanji samples were classified according to paper mulberry type, wherein the prediction accuracies of PLS-DA, SVM, and RF were 100%, 100%, and 98%, respectively. These results suggested that NIR in combination with multivariate statistical methods can be used for hanji material classification.
- Subjects :
- Environmental Engineering
business.industry
Near-infrared spectroscopy
Bioengineering
Pattern recognition
Raw material
Linear discriminant analysis
Random forest
Support vector machine
Statistical classification
Partial least squares regression
Artificial intelligence
Multivariate statistical
business
Waste Management and Disposal
Mathematics
Subjects
Details
- ISSN :
- 19302126
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- BioResources
- Accession number :
- edsair.doi...........9470d80fcbd01acd587f6762720d41c6