Predicting Extractives, Lignin, and Cellulose Contents Using Near Infrared Spectroscopy on Solid Wood in<i>Eucalyptus globulus</i>

Poke, Fiona S.; Raymond, Carolyn A

doi:10.1080/02773810600732708

Cited by 107 publications

(70 citation statements)

References 10 publications

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“…Given Near-infrared (NIR) spectroscopy it is one of the most powerful non-destructive techniques, has been applied to the determination of various properties in numerous materials, such as food (Wang et al, 2014;Fu et al, 2012), ethanol (Xu et al, 2013), and wood lignin (Zhang et al, 2011;Yao et al, 2010;Alves et al, 2006;Hodge & Woodbridge, 2004;Poke et al, 2004;Schwanninger & Hinterstoisser, 2001) and cellulose (Tyson et al, 2010;Hein et al, 2009;Poke et al, 2006), wood properties and basic density (Santos et al, 2012a;Alves et al, 2012;Sykes et al, 2005;Kelley et al, 2004;Hauksson et al, 2001), pulp yield, kappa number and mechanical and optical properties of pulps Downes et al, 2009;Schimleck et al, 2006;Fardim et al, 2005;Schimleck et al, 2005;Fardim et al, 2002).…”

Section: Spectra Collection and Data Processingmentioning

confidence: 99%

Estimation of Acacia melanoxylon unbleached Kraft pulp brightness by NIR spectroscopy

Santos¹,

Anjos

Pereira³

2015

For. syst.

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Aim of the study: The ability of NIR spectroscopy for predicting the ISO brightness was studied on unbleached Kraft pulps of Acacia melanoxylon R. Br.Area of study: Sites covering littoral north, mid interior north and centre interior of Portugal. Materials and methods:The samples were Kraft pulped in standard identical conditions targeted to a kappa number of 15. A Near Infrared (NIR) partial least squares regression (PLSR) model was developed for the ISO brightness prediction using 75 pulp samples with a variation range of 18.9 to 47.9 %.Main results: Very good correlations between NIR spectra and ISO brightness were obtained. Ten methods were used for PLS analysis (cross validation with 48 samples), and a test set validation was made with 27 samples. The 1stDer pre-processed spectra coupling two wavenumber ranges from 9404 to 7498 cm -1 and 4605 to 4243 cm -1 allowed the best model with a root mean square error of ISO brightness prediction of 0.5 % (RMSEP), a r 2 of 99.5 % with a RPD of 14.7. Research highlights: According to AACC Method 39-00, the present model is sufficiently accurate to be used for process control (RPD ≥ 8)

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Section: Spectra Collection and Data Processingmentioning

confidence: 99%

Estimation of Acacia melanoxylon unbleached Kraft pulp brightness by NIR spectroscopy

Santos¹,

Anjos

Pereira³

2015

For. syst.

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“…It has been increasingly used in food industry, petrochemical industry, medicine and other fields [1]- [6]. In recent years, it has been applied more and more in wood science research [7]- [10]. The determination of the chemical composition of wood has to consume a lot of manpower, material resources and time.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Model Method in Near Infrared Spectra Analysis

2018

Oct. 17-19, 2017 Dubai (UAE)

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“…It is a powerful analytical tool used in the wood industry for predicting chemical composition of wood (Poke and Raymond 2006), classifying the type of fungal decay in wood (Fackler et al 2007;Yang et al 2008), discriminating blue-stained wood (Via et al 2008), and identifying wood species (Tsuchikawa et al 2003;Shou et al 2014). Recently, NIRS has been used to detect knots on wood surfaces.…”

Section: Introductionmentioning

confidence: 99%

Detection of Knot Defects on Coniferous Wood Surface Using Near Infrared Spectroscopy and Chemometrics

et al. 2016

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Lumber pieces usually contain defects such as knots, which strongly affect the strength and stiffness. To develop a model for rapid, accurate grading of lumbers based on knots, Douglas fir, spruce-pine-fir (SPF), Chinese hemlock, and Dragon spruce were used. The experiments explored the effects of modelling methods and spectral preprocess methods for knot detection, and investigated the feasibility of using a model built within one species to discriminate the samples from other species, using a novel variable selection method-random frog (RF)-to select effective wavelengths. The results showed that least squares-support vector machines coupled with first derivative preprocessed spectra achieved best performance for both single and mixed models. Models built within Dragon spruce could be used to classify knot samples from SPF and Chinese hemlock but not Douglas fir, and vice versa. Eight effective wavelengths (1314 nm, 1358 nm, 1409 nm, 1340 nm, 1260 nm, 1586 nm, 1288 nm, and 1402 nm) were selected by RF to build effective wavelengths based models. The sensitivity, specificity, and accuracy in the validation set were 98.49%, 93.42%, and 96.30%, respectively. Good results could be obtained when using data at just eight wavelengths, as an alternative to evaluating the whole spectrum.

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Predicting Extractives, Lignin, and Cellulose Contents Using Near Infrared Spectroscopy on Solid Wood inEucalyptus globulus

Cited by 107 publications

References 10 publications

Estimation of Acacia melanoxylon unbleached Kraft pulp brightness by NIR spectroscopy

Estimation of Acacia melanoxylon unbleached Kraft pulp brightness by NIR spectroscopy

A Multi-Model Method in Near Infrared Spectra Analysis

Detection of Knot Defects on Coniferous Wood Surface Using Near Infrared Spectroscopy and Chemometrics

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