Possibility of Wood Classification in Korean Softwood Species Using Near-infrared Spectroscopy Based on Their Chemical Compositions

Abstract

This study was to establish the interrelation between chemical compositions and near infrared (NIR) spectra for the classification on distinguishability of domestic gymnosperms. Traditional wet chemistry methods and infrared spectral analyses were performed. In chemical compositions of five softwood species including larch (Larix kaempferi), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), cypress (Chamaecyparis obtusa), and cedar (Cryptomeria japonica), their extractives and lignin contents provided the major information for distinction between the wood species. However, depending on the production region and purchasing time of woods, chemical compositions were different even though in same species. Especially, red pine harvested from Naju showed the highest extractive content about 16.3%, whereas that from Donghae showed about 5.0%. These results were expected due to different environmental conditions such as sunshine amount, nutrients and moisture contents, and these phenomena were also observed in other species. As a result of the principal component analysis (PCA) using NIR between five species (total 19 samples), the samples were divided into three groups in the score plot based on principal component (PC) 1 and principal component (PC) 2; group 1) red pine and Korean pine, group 2) larch, and group 3) cypress and cedar. Based on the chemical composition results, it was concluded that extractive content was highly relevant to wood classification by NIR analysis.

Characteristics of sago pith and sago bark waste from Seram Island, Maluku, Indonesia

“…Extractive substances consist of various organic compounds such as triglycerides, fatty acids and sterols (Nascimento et al, 2013) and phenolic compounds such as tannins (Fava et al, 2006), flavonoids (Sirmah et al 2009) with low molecular weight which are found in the lumen and part of the cell wall. The amount and type of plant extractive substances depend on their location (Grabner et al 2005), type of plant, place of growth and climatic conditions (Park et al 2017). Extractive substances that can dissolve in organic solvents such as benzene alcohol solutions include waxes, fats, resins, oils, and tannins as well as certain components that are not soluble in ether (Sjostrom 1998).…”

“…This was because SBW's lignin was higher than SPW (Table 1). Cellulose and hemicellulose were indicated by absorption at wavenumber of 1,400-875 cm -1 , lignin at 1,700-1,200 cm -1 and extractive at 1,700 cm -1 (Park et al 2017). Cellulose is indicated by the high absorption of the-OH and C-O bonds, hemicellulose is indicated by the absorption of compounds with C=O double bonds (Yang et al 2007) while lignin is indicated by the high absorption of single bonds and carbon double bonds.…”

“…Biomass can be obtained from various agricultural and forestry wastes such as sago waste (Metroxylon sp.). However, biomass has specific properties according to the type, place of growth, and environmental conditions (Park et al 2017). Therefore, it is necessary to understand biomass properties before it is utilized.…”

Classification of softwoods using wood extract information and near infrared spectroscopy

“…Over the past few years, the authors collected realtime information on wood analysis using NIR spectroscopy. In particular, the authors' research group reported the analyses of the physical and chemical properties of Korean coniferous species used as interior and exterior materials in architecture, leading to automatic classification of the species (Kwon et al 2017(Kwon et al , 2019Park et al 2017Park et al , 2018Yang et al 2017Yang et al , 2019aYeon et al 2019;Yang et al 2020). The Korean species used in the study were derived not only from one specific region but also from various latitudinal regions within Korea, and involved Larix kaempferi (Larch), Pinus densiflora (red pine), Pinus koraiensis (Korean pine), Cryptomeria japonica (cedar), and Chamaecyparis obtuse (cypress).…”

Application of neural networks for classifying softwood species using near infrared spectroscopy

“…A study on the classification of wood species using NIR spectroscopy has developed a model by performing multivariate linear regression on spectra acquired from powder or a solid lumber surface. 26–31 Since wood has different light-scattering properties depending on the direction of incidence due to the anisotropic cell structure, 26,32 it is more accurate to apply the model using a powder sample with higher homogeneity. Nevertheless, to perform a non-destructive measurement, which is a prior advantage of NIR spectroscopy, it is necessary to classify the species as solid lumber.…”