Classification of softwoods using wood extract information and near infrared spectroscopy

Park, Se-Yeong; Kim, Jong-Hwa; Kim, Jong-Chan; Yang, Sejung; Yeo, Hwanmyeong; Choi, In-Gyu

doi:10.15376/biores.16.3.5301-5312

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…A significant peak at about 1675 nm occurred in PC-3, which was associated with the first overtone of C ar -H stretching vibrations from the lignin aromatic groups [23]. Park and Yeon et al reported that the contents and types of extractives can affect the identification of softwood using NIR spectra [24][25][26]. The NIR spectral features contain extractive information, and the extractives played an important role in identifying the 25 timbers in the proposed method.…”

Section: Wood Identification Based On Shorter Nir Band Fused Glcm Featuresmentioning

confidence: 99%

Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features

Pan

Chen

et al. 2021

Forests

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Identifying wood accurately and rapidly is one of the best ways to prevent wood product fakes and adulterants in forestry products. Wood identification traditionally relies heavily on special experts that spend extensive time in the laboratory. A new method is proposed that uses near-infrared (NIR) spectra at a wavelength of 780–2300 nm incorporated with the gray-level co-occurrence (GLCM) texture feature to accurately and rapidly identify timbers. The NIR spectral features were determined by principal component analysis (PCA), and the digital image features extracted with the GLCM were used to create a support vector machine (SVM) model to identify the timbers. The results from fusion features of raw spectra and four GLCM features of 25 timbers showed that identification accuracy by the model was 99.43%. A sample anisotropy and heterogeneity comparative analysis revealed that the wood identification information from the transverse surface had more characteristics than that from the tangential and radial surfaces. Furthermore, short-wavelength pre-processed NIR bands of 780–1100 nm and 1100–2300 nm realized high identification accuracy of 99.43% and 100%, respectively. The four GLCM features were effective for improving identification accuracy by improving the data spatial clustering features.

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Section: Wood Identification Based On Shorter Nir Band Fused Glcm Featuresmentioning

confidence: 99%

Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features

Pan

Chen

et al. 2021

Forests

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show abstract

“…Among these classification schemes, the spectral analysis scheme has the advantages of fast speed, high accuracy, and non-destructive testing. For example, Park et al (2021) classified three coniferous wood species by use of wood extract and its near-infrared spectrum. Ma et al (2021) proposed a new classification for 15 wood species based on spatially resolved diffuse reflection (i.e., wavelength range 600 to 1000 nm).…”

Section: Introductionmentioning

confidence: 99%

Effect of wood surface finish on wood species classification using spectral reflectance

Wang,

Zhao,

Yang

2024

BioRes

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Wood species can be classified by spectral reflectance. It is unclear whether finish coated on the wood surface affects the accuracy of wood species classification. This paper focused on this issue, using the spectral reflectance of 8 different kinds of finish for wood species classification. The spectral reflectance of wood surface coated with finish was modified by the transfer model in order to reduce the effect of finish on classification accuracy. The experimental results show that it is not feasible to use the spectral reflectance of wood samples coated with finish directly to classify wood species; the best classification accuracy using the eight finishes was 30%. After correcting the spectrum of wood samples coated with finishes with the direct standardization (DS) transfer model, the classification accuracy of the near-infrared (NIR) spectrum was close to that of the original spectrum without finish. However, the visible/near-infrared (VIS/NIR) spectrum did not achieve a good classification effect after correction with the DS transfer model.

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A Method for Digital Resource Allocation in Mobile Online Education Based on Ant Colony Algorithm

Huang,

Geng

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Classification of softwoods using wood extract information and near infrared spectroscopy

Cited by 6 publications

References 20 publications

Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features

Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features

Effect of wood surface finish on wood species classification using spectral reflectance

A Method for Digital Resource Allocation in Mobile Online Education Based on Ant Colony Algorithm

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