Comparison of Pinus taeda L. wood property calibrations based on NIR spectra from the radial-longitudinal and radial-transverse faces of wooden strips

Schimleck, Laurence R.; Stürzenbecher, Reinhard; Mora, Christian; Jones, Paul D.; Daniels, Richard F.

doi:10.1515/hf.2005.034

Cited by 39 publications

(24 citation statements)

References 14 publications

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“…lumber from spectra collected on transversal sections were better than models based on spectra acquired over tangential or radial sections. The difference between transversal and radial measurements was lower in the case of density estimation (Schimleck et al 2005, Defo et al 2007, Fujimoto et al 2008. Tsuchikawa et al (1996a) explained the difference between tangential and transversal measurements by the fact that when scanning the transversal section, the incident radiation directly interferes with the free water present in the lumen of the cell, while it is not the case when scanning the tangential or the radial section since the radiation will first encounter the cell wall.…”

Section: Principles Of Nir Spectroscopymentioning

confidence: 99%

A review of near-infrared spectroscopy for monitoring moisture content and density of solid wood

Leblon

Adedipe

Hans

et al. 2013

The Forestry Chronicle

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This review article examines past and current research on the application of near-infrared (NIR) reflectance/transmittance spectroscopy (NIRS) for real-time monitoring of moisture content and density of solid wood. Most of the applications of NIRS on solid wood have focussed on the application of multivariate statistics as exploratory tools for the prediction of physical, chemical and mechanical properties, such as moisture content, density, stiffness, cellulose and lignin content. However, very few studies on the development of optical models and the use of NIRS transmittance techniques on solid wood have been reported. NIRS technology has the potential to be used as a rapid tool that could be employed for at-line measurement and monitoring of wood properties in the forest products industry.Keywords: wood properties, near-infrared spectroscopy, multivariate statistics, optical model, transmittance, reflectance résumé Cet article de revue examine la recherche passée et actuelle sur l'application de la spectroscopie proche infra-rouge en mode réflectance/transmittance pour le suivi en temps réel de la teneur en eau et de la densité du bois solide. La plupart des applications de la spectroscopie proche infra-rouge sur le bois solide ont porté sur l'application des méthodes statistiques multi-variées pour l' estimation des propriétés physiques, chimiques et mécaniques, telles que la teneur en eau, la densité, la dureté, le contenu en cellulose et en lignine. Cependant, peu d' études ont porté sur le développement de modèles optiques et sur l'utilisation de la spectroscopie proche infra-rouge en mode transmittance pour le bois solide. La spectroscopie proche infra-rouge a le potentiel d' être utilisée comme un outil rapide de mesure et suivi en temps réel des propriétés du bois dans l'industrie des produits forestiers.

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Section: Principles Of Nir Spectroscopymentioning

confidence: 99%

A review of near-infrared spectroscopy for monitoring moisture content and density of solid wood

Leblon

Adedipe

Hans

et al. 2013

The Forestry Chronicle

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“…The aim of this treatment was also to conserve the wood prior to NIR measurement and future nondestructive analyses. Schimleck et al (2003) removed extractives in wood samples by extracting them during 18 h with warm acetone (C 3 H 6 O) containing C=O double bonds and C-H bonds.…”

Section: Wood Samples and Determination Of Wood Propertiesmentioning

confidence: 99%

“…The Q 2 gives the amount of explained variation and is dimensionless as is the ratio of performance to deviation (RPD), which reflects the predicative ability of the calibration. RPD values based on independent test sets and in the range of 1.5-2.5 indicate that calibrations can be used for coarse screening purposes (Schimleck et al 2003). Values >2.5 are satisfactory for screening, whereas models with RPD values >10 have excellent performance (Williams and Sobering 1993).…”

Section: Model Diagnostics and Softwarementioning

confidence: 99%

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Prediction ofPinus sylvestrisclear-wood properties using NIR spectroscopy and biorthogonal partial least squares regression

et al. 2008

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Thirteen wood parameters were predicted using near infrared (NIR) spectra in the range 780-2380 nm modelled by biorthogonal partial least squares regression. The analysis of parameters and NIR measurements was done on clearwood samples from the base and midstem of Scots pine (Pinus sylvestris L.) from trees at two sites. Calibrations based on the measured parameters at seven growth rings (cambial age ranging between 6 and 42 years) could be divided into three groups: (i) the best accuracy was found for longitudinal modulus of elasticity (r > 0.9) followed by bending, compression, and cell length (0.8 < r < 0.9); (ii) microfibril angle, longitudinal hardness, proportion of latewood, and creep with correlations in the range of 0.7-0.8; and (iii) tangential hardness, cell diameter, and cell wall thickness with 0.4 < r < 0.7. It was also shown that juvenile (cambial age 20 years) and mature wood can be classified using NIR techniques.Résumé : La régression bi-orthogonale partielle par les moindres carrés a été utilisée pour modéliser 13 paramètres du bois à l'aide de spectres de transmittance dans les longueurs d'onde allant de 780 à 2380 nm dans le proche infrarouge (PIR). L'analyse des paramètres et des mesures de transmittance dans le PIR a été réalisée sur des échantillons de bois sain provenant de la base du tronc et de la portion intermédiaire de la cime de tiges de pin sylvestre (Pinus sylvestris L.) récoltées dans deux stations. Les étalonnages basés sur la mesure des paramètres dans sept cernes annuels (âge cambial variant de 6 à 42 ans) pouvaient être divisés en trois groupes : (i) la plus grande précision a été obtenue pour le module d'élasticité longitudinal (r > 0,9) suivi par la flexion, la compression et la longueur des cellules (0,8 < r < 0,9), (ii) l'angle des microfibrilles, la dureté longitudinale, la proportion de bois final et le fluage de retour avaient un coefficient de corré-lation de 0,7-0,8 et (iii) la dureté tangentielle, le diamètre des cellules et l'épaisseur des parois cellulaires avaient un coefficient de corrélation variant entre 0,4 et 0,7. Nous avons aussi montré que l'utilisation de la transmittance dans le PIR permet de distinguer le bois juvénile (âge cambial 20 ans) du bois mature.[Traduit par la Rédaction]

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“…In addition, the relationship between the NIR spectra and the surface roughness has been studied for Pinus taeda (Schimleck et al 2005). The cited study reported that surface roughness contributed to weaker statistics and that the surface roughness of Pinus taeda has a weak influence on the effect of models built by the NIR data.…”

Section: Introductionmentioning

confidence: 99%

Correlation of Near Infrared Spectroscopy Measurements with the Surface Roughness of Wood

Zhang¹,

Liu²,

Yang³

2015

BioResources

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The surface roughness of Chinese fir and Eucalyptus wood samples were measured using the stylus profile method in order to investigate the correlation between near infrared (NIR) spectroscopy and surface roughness. The results showed that the NIR spectra absorption showed differences among samples from different surface roughnesses, and the absorption decreased with the increase of the surface roughness. A strong relationship was found between the surface roughness parameters, i.e., the arithmetical mean deviation of the profile (Ra), the ten-point height of irregularities (Rz), and the maximum height of profile (Ry). Based on the NIR spectra of the Chinese fir wood samples and the mixed wood samples of the two wood species, and the correlation coefficients of these two types of wood samples in a calibration set were 0.77 to 0.83 and 0.67 to 0.74, respectively. A relatively poor correlation was found in the model based on the Eucalyptus samples; however, it was still significant. These results suggested that there was relative information about the surface roughness from the NIR spectra, which further illustrated that the surface roughness may influence the effect of models for wood properties built by NIR data.

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Comparison of Pinus taeda L. wood property calibrations based on NIR spectra from the radial-longitudinal and radial-transverse faces of wooden strips

Cited by 39 publications

References 14 publications

A review of near-infrared spectroscopy for monitoring moisture content and density of solid wood

A review of near-infrared spectroscopy for monitoring moisture content and density of solid wood

Prediction ofPinus sylvestrisclear-wood properties using NIR spectroscopy and biorthogonal partial least squares regression

Correlation of Near Infrared Spectroscopy Measurements with the Surface Roughness of Wood

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