DEVELOPING NEAR INFRARED SPECTROSCOPIC MODELS FOR PREDICTING DENSITY OF Eucalyptus WOOD BASED ON INDIRECT MEASUREMENT

Arriel, Taiana Guimarães; Ramalho, Fernanda Maria Guedes; Lima, Rebeca Alves Barreto; Sousa, Kelly Iapuque Rodrigues de; Hein, Paulo Ricardo Gherardi

doi:10.1590/01047760201925032646

Cited by 14 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies have reported that wood density can be predicted from NIR signatures (Hein et al 2010, Arriel et al 2019. In this study, the best model for esti-iForest 15: 372-380 374 This kind of predictive model presents many industrial applications since wood density offers considerable information about wood.…”

Section: Predicting Wood Properties From Nir Modelsmentioning

confidence: 97%

NIR-based models for estimating selected physical and chemical wood properties from fast-growing plantations

Loureiro¹,

Arriel²,

Ramalho³

et al. 2022

iForest

View full text Add to dashboard Cite

As a faster, reliable, and low cost technique, applicable to large samplings, near infrared (NIR) spectroscopy technology has been widely applied for highthroughput phenotyping in forest breeding programmes. The aim of this study was to develop multivariate models for estimating the chemical and physical properties of juvenile wood based on NIR signatures of milled wood. Moreover, two approaches, namely, external validation by clone and by age, were tested to validate the model for estimating extractive content. NIR spectra of wood specimens taken from three clones of Eucalyptus urophylla (one to six years old) grown in southern Brazil were used to calibrate and validate models for predicting the wood basic density, total extractives, ash content, holocellulose content, syringyl to guaiacyl ratio (S/G) and elementary components of the wood. PLS-R models were validated by an independent set of wood specimens and presented promising statistics for the estimating wood density (R 2 p = 0.768), extractives (R 2 p = 0.912), ash (R 2 p = 0.936) and carbon (R 2 p = 0.697) contents from NIR signatures measured in the milled wood of young trees. Furthermore, NIR models for estimating the extractive content of wood were validated using the clones or ages left out of the training sets. Most models presented satisfactory statistics (R 2 > 90%) and could be applied to routine laboratory analyses or to select potential trees in Eucalyptus breeding programmes.

show abstract

Section: Predicting Wood Properties From Nir Modelsmentioning

confidence: 97%

NIR-based models for estimating selected physical and chemical wood properties from fast-growing plantations

Loureiro¹,

Arriel²,

Ramalho³

et al. 2022

iForest

View full text Add to dashboard Cite

show abstract

“…Studies in the literature found better statistics when excluding the region of the spectrum from 12500 to 9000 cm -1 and 4000 to 3600 cm -1 for presenting noise and difficulty in acquiring information (Belini et al, 2011;Costa et al, 2018b;Arriel et al, 2019). Thus, this region was excluded and the analysis performed only in the region from 9000 to 4000 cm -1 (Table 5 -models 6 to 10).…”

Section: Pls-r and Pls-da Models To Predict Paper Propertiesmentioning

confidence: 99%

“…Many studies have been developed to evaluate forest products such as wood (Tsuchikawa and Kobori, 2015;Costa et al, 2018a;Arriel et al, 2019;Rosado et al, 2019), engineered panels (Via, 2010;Belini et al, 2011;Huang et al, 2019), charcoal (Costa et al, 2018b) and pulp (Costa et al, 2019). In terms of cellulosic pulp, research involving NIR spectroscopy began with the estimation of pulp kappa number (Birkett and Gambino, 1989).…”

Section: Introductionmentioning

confidence: 99%

Near infrared spectroscopy for estimating properties of kraft paper reinforced with cellulose nanofibrils

Lima

Costa

Carvalho

et al. 2022

CERNE

Self Cite

View full text Add to dashboard Cite

Background:The aim of this study was to investigate near infrared (NIR) spectroscopy ability to estimate nanofibril concentration, physical and mechanical properties of Kraft paper reinforced with cellulose nanofibrils (CNF). For this purpose, paper sheets were prepared by mixing unbleached Eucalyptus fibers and cellulose nanofibrils. Twelve treatments result from the combination of the nominal grammages 75, 85, 95 and 105 g m -2 with the nanofibril concentrations of 1, 5 and 10wt%, with 7 sheets per treatment. NIR spectra measured directly on paper specimens were correlated with physical and mechanical properties values obtained through conventional laboratory analyzes.Results: Principal component analysis (PCA) revealed no separation among specimens related to nanofibril content. Partial least squares regression (PLS-R) models for estimating nanofibril content, tensile index, stretch and resistance to air passage yielded R²cv ranging from 0.73 to 0.98. Partial least squares -discriminant analysis (PLS-DA) correctly classified up to 93% of the paper specimens both by grammage and nanofibril content using NIR spectra. Conclusion:This approach appears to be suitable for predicting physical and mechanical properties of Kraft papers and can detect cellulose nanofibril content in the cellulose handsheets.

show abstract

“…NIR spectra are correlated with material composition or properties determined by standardised methods using multivariate tools in order to generate a predictive model (Meder et al 2010). Multivariate regression models developed from NIR spectra have been successfully used to estimate wood density across a range of species (Schimleck et al 1999;Gindl et al 2001;Schimleck et al 2005;Jiang et al 2006;Jones et al 2006;Mora et al 2008;Hein et al 2009;, Arriel et al 2019.…”

Section: Introductionmentioning

confidence: 99%

Evaluating basic density calibrations based on NIR spectra recorded on the three wood faces and subject to different mathematical treatments

Amaral

Santos

Hein

et al. 2021

NZJFS

View full text Add to dashboard Cite

Background: Near infrared (NIR) spectroscopy has been successfully applied to estimate the chemical, physical and mechanical properties of various biological materials, including wood. This study aimed to evaluate basic density calibrations based on NIR spectra collected from three wood faces and subject to different mathematical treatments. Methods: Diffuse reflectance NIR spectra were recorded using an integrating sphere on the transverse, radial and tangential surfaces of 278 wood specimens of Eucalyptus urophylla x Eucalyptus grandis. Basic density of the wood specimens was determined in the laboratory by the immersion method and correlated with NIR spectra by Partial Least Squares regression. Different statistical treatments were then applied to the data, including Standard Normal Variate, Multiplicative Scatter Correction, First and Second Derivatives, Normalization, Autoscale and MeanCenter transformations. Results: The predictive model based on NIR spectra measured on the transverse surface performed the best (R²cv = 0.85 and RMSE = 25.5 kg/m³) while the model developed from the NIR spectra measured on the tangential surface had the poorest performance (R²cv = 0.53 and RMSE = 46.8 kg/m³). The difference in performance between models based on original (untreated) and mathematically-treated spectra was minimal. Conclusions: Multivariate models fitted to NIR spectra were found to be efficient for predicting the basic density of Eucalyptus wood, especially when based on spectra measured on the transversal surface. For this data set, models based on the original spectra and mathematically treated spectra had similar performance. The reported findings show that mathematical transformations are not always able to extract more information from the spectra in the NIR.

show abstract

DEVELOPING NEAR INFRARED SPECTROSCOPIC MODELS FOR PREDICTING DENSITY OF Eucalyptus WOOD BASED ON INDIRECT MEASUREMENT

Cited by 14 publications

References 24 publications

NIR-based models for estimating selected physical and chemical wood properties from fast-growing plantations

NIR-based models for estimating selected physical and chemical wood properties from fast-growing plantations

Near infrared spectroscopy for estimating properties of kraft paper reinforced with cellulose nanofibrils

Evaluating basic density calibrations based on NIR spectra recorded on the three wood faces and subject to different mathematical treatments

Contact Info

Product

Resources

About