A common near infrared—based partial least squares regression model for the prediction of wood density of Pinus pinaster and Larix × eurolepis

Alves, Ana; Santos, António J.; Rozenberg, Philippe; Pâques, Luc; Charpentier, Jean-Paul; Schwanninger, Manfred; Rodrigues, José Carlos

doi:10.1007/s00226-010-0383-x

Cited by 82 publications

(55 citation statements)

References 45 publications

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“…As reported in previous research (Alves et al 2012), the absorbance at about 1448 nm is bound up with the first overtone of O-H stretching vibration from phenolic groups of lignin (Fackler and Schwanninger 2010).…”

Section: Nir Spectra Analysissupporting

confidence: 71%

Non-Contact Detection of Surface Quality of Knot Defects on Eucalypt Veneers by Near Infrared Spectroscopy Coupled with Soft Independent Modeling of Class Analogy

et al. 2015

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A knot is a natural defect that degrades the quality of softwood and hardwood veneer. To improve efficiency, the plywood industry needs a rapid, inexpensive method of knot identification that is easy to operate and industrialize. Although a non-contact knot-detection technology based on NIR spectroscopy and soft independent modeling of class analogy (SIMCA) has been successful in detecting softwood knots, it has not yet been explored in eucalypt (hardwood) veneer. This study investigated the interaction between knot size, spectral pretreatment methods, and wavelength range selections on this model's classification accuracy of knots and normal eucalypt wood. The study found that classification results were accurate up to 94.4% for large knot samples (10 to 15 mm in diameter) and up to 100% for knot-free samples. Spectral data for small knots (< 5 mm in diameter) impeded the model's classification accuracy because of confusion between small knots and both large knots and normal wood. Calibration models developed with second-derivative spectra exhibited the highest accuracy, followed by models built with first-derivative spectra, models based on spectra transformed by vector normalization, and the model based on the raw spectroscopy. Wavelength ranges of 1100 to 2500 nm enabled greater classification accuracy than wavelength ranges of 780 to 1100 nm or 780 to 2500 nm.

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Section: Nir Spectra Analysissupporting

confidence: 71%

Non-Contact Detection of Surface Quality of Knot Defects on Eucalypt Veneers by Near Infrared Spectroscopy Coupled with Soft Independent Modeling of Class Analogy

et al. 2015

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“…Absorbance bands of NIR spectra Fig. 3 (A) showed the results of the raw The spectral region ranging from 1000-1200 nm is represented as lignin band assignment reported in previous study (Alves et al, 2012). In 1200-1600 nm region, bands related to cellulose component are mainly observed (Schwanninger et al, 2011).…”

Section: Classification Of Wood Based On Nir Spectroscopymentioning

confidence: 77%

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

Park¹,

Kim²,

Kim³

et al. 2017

Journal of the Korean Wood Science and Technology

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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.

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“…A partial least squares regression model was implemented for calibration, using the OPUS Quant software from Bruker Optics (see details in Alves et al 2010). Finally, we were able to obtain accurate data on wood extractives, lignin content and H/G ratio.…”

Section: Wood Qualitymentioning

confidence: 99%

Ecological and association genetics in two Mediterranean pine species

Quilón¹

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A common near infrared—based partial least squares regression model for the prediction of wood density of Pinus pinaster and Larix × eurolepis

Cited by 82 publications

References 45 publications

Non-Contact Detection of Surface Quality of Knot Defects on Eucalypt Veneers by Near Infrared Spectroscopy Coupled with Soft Independent Modeling of Class Analogy

Non-Contact Detection of Surface Quality of Knot Defects on Eucalypt Veneers by Near Infrared Spectroscopy Coupled with Soft Independent Modeling of Class Analogy

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

Ecological and association genetics in two Mediterranean pine species

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