Application of Near-Infrared Spectroscopy to Determine the Juvenile–Mature Wood Transition in Black Spruce

Giroud, Guillaume; Defo, Maurice; Bégin, Jean; Ung, Chhun-Huor

doi:10.13073/fpj-d-14-00045

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…Near-infrared (NIR) spectroscopy, which is an advanced NDT technology, also shows promise for the application, as transmitted or reflected spectra of electromagnetic waves ranging from 800 to 2500 nm have been used for the online or on-site measurement of various organic materials, such as agricultural products, foods, polymers, textiles, pharmaceuticals, and petrochemicals (Tsuchikawa and Kobori 2015). NIR spectroscopy has been used and developed, for the prediction of mechanical wood properties, by many scientist over the past 20 years (Hoffmeyer and Pedersen 1995;Thumm and Meder 2001;Meder et al 2003;Tsuchikawa et al 2005;Fujimoto et al 2007;Fujimoto et al 2008Fujimoto et al , 2013Fujimoto et al , 2015Giroud et al 2015). Consequently, an online NIR spectroscopic system has been developed to enable rapid processing by the wood industry, which has allowed a range of wood properties to be predicted in various species, including the MOE of pine (Pinus radiata) lumber (Meder et al 2003); the MOE, bending strength, density, and moisture content (MC) of Japanese larch/karamatsu (Larix kaempferi) lumber (Fujimoto et al 2010a); the MOE and MC of hinoki (Chamaecyparis obtusa) lumber (Kobori et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Modulus of elasticity prediction model on sugi (Cryptomeria japonica) lumber using online near-infrared (NIR) spectroscopic system

Sofianto

Inagaki

Kato

et al. 2017

International Wood Products Journal

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In this study, static bending measurements and online NIR spectra acquisitions were combined to construct modulus of elasticity (MOE) prediction model for sugi lumber. NIR spectra were acquired from tangential surface of sugi lumbers at a speed of 120 m min −1 to assess its effectiveness in the wood industry. Cross-validation partial least squares regression (CV-PLSR) and test-set-validation partial least squares regression (TSV-PLSR) analyses were employed for analysing the data. The second derivative (2d) spectra with 19 smoothing points (Savitzky-Golay algorithm, second polynomial) gave the best result as spectral pre-processing treatment with the lowest root mean square error of cross-validation and the highest coefficient of determination for cross-validation based on the optimum number of latent variables as assessed from the minimum validation residual variance value in the CV-PLSR analysis. These 2d spectra were then used in the TSV-PLSR analysis for 100 repetitions to check the robustness of the calibration.

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Section: Introductionmentioning

confidence: 99%

Modulus of elasticity prediction model on sugi (Cryptomeria japonica) lumber using online near-infrared (NIR) spectroscopic system

Sofianto

Inagaki

Kato

et al. 2017

International Wood Products Journal

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“…These profiles are valuable to determine the transition age from juvenile to mature wood, and to calculate their relative proportions as investigated by Giroud et al. 18 Juvenile wood, i.e. the first few growth rings from the pith, is undesirable for many industrial applications.…”

Section: Resultsmentioning

confidence: 99%

“…17 Radial profiles of these wood properties are suitable to characterize the stage of maturity of trees and the proportions of juvenile and mature wood. 18 As a second objective, the average values of wood properties estimated by NIR spectroscopy were compared with those measured by SilviScan using a Bland and Altman analysis. 19 NIR calibrations were also performed using one average spectrum per wood sample for comparison purposes.…”

Section: Introductionmentioning

confidence: 99%

Determination of radial profiles of wood properties using a near infrared scanning system

Giroud

Defo

Bégin

2020

Journal of Near Infrared Spectroscopy

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A near infrared (NIR) spectrometer, equipped with a 2-mm diameter fiber optic probe, was used to develop a rapid and automated method for determining the radial profiles of wood properties for six tree species of the boreal forest of eastern Canada. Partial least squares regressions were performed using individual spectra, collected every 5 mm, from the pith to the bark, using 1,538 samples previously scanned by SilviScan to provide reference data. Results obtained for density (r2 of 0.57 to 0.65; SE of 21 to 33 kg/m3), microfibril angle (0.56 to 0.82, 2.3 to 4.8°) and modulus of elasticity (0.71 to 0.85, 1.3 to 1.8 GPa) were suitable for the purpose of this study. This method was then applied to more than 30,000 tree cores and the resulting radial profiles of the properties were consistent with literature. Area-weighted average predictions also gave reliable estimates at the disk scale. NIR-estimated values were strongly correlated to the measured density (r of 0.83 to 0.93) and modulus of elasticity (0.82 to 0.88) and moderately correlated to measured microfibril angle (0.64 to 0.78). Radial profiles of wood properties are needed to characterize the proportions of juvenile and mature wood. The developed method therefore offers interesting opportunities for estimating the quality of wood fiber in trees growing in plantation or natural forest.

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“…This means that the improvement is mostly due to basic density since log diameter combined with bark thickness did not improve the results. Hence, future research could focus on combining Near Infrared (NIR) spectroscopy and GPR sensors for improving the measurement, given that NIR can produce good results for wood density prediction [30,31].…”

Section: Log Characteristics and Wood Properties As Additional Predic...mentioning

confidence: 99%

Using Ground Penetrating Radar (GPR) to Predict Log Moisture Content of Commercially Important Canadian Softwoods

Duchesne,

Tong,

Hans

2023

Forests

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The non-destructive testing of wood fibre properties is crucial for informing forest management decisions and achieving optimal resource utilization. Moisture content (MC) is an important indicator of wood freshness and may reveal the presence of wood degradation. However, efficient methods are still needed to better monitor this property along the forest–wood value chain. The objective of the study was to develop prediction models to evaluate log MC based on the propagation of ground penetrating radar (GPR) signals. A total of 165 trees representing four species (black spruce (Picea mariana (Mill.) B.S.P.), white spruce (Picea glauca (Moench) Voss), red spruce (Picea rubens Sarg.), and balsam fir (Abies balsamea (L.) Mill.)) were harvested in two regions of the province of Quebec. GPR signals were acquired in the green (fresh) state and at three subsequent drying stages. Partial least squares regression (PLSR) and locally weighted PLSR (LWPLSR) were employed to establish relationships between GPR signals (antenna frequency: 1.6 GHz) and log properties. The models were fitted on three calibration sets containing four drying stages and different species mixes. The LWPLSR models performed better than the PLSR models for predicting log MC, with a lower root mean square error (RMSEp range: 10.8%–20.2% vs. 13.0%–20.5%) and a higher R2p (0.63–0.87 vs. 0.62–0.82). Spruce-only models performed considerably better than fir-only models while multi-species models were in-between. Despite the complex anisotropy of wood and the physics of wave propagation, the GPR technology can be successfully used to estimate log moisture content, but the GPR-based MC models should be calibrated for each specific type of wood material.

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Application of Near-Infrared Spectroscopy to Determine the Juvenile–Mature Wood Transition in Black Spruce

Cited by 7 publications

References 24 publications

Modulus of elasticity prediction model on sugi (Cryptomeria japonica) lumber using online near-infrared (NIR) spectroscopic system

Modulus of elasticity prediction model on sugi (Cryptomeria japonica) lumber using online near-infrared (NIR) spectroscopic system

Determination of radial profiles of wood properties using a near infrared scanning system

Using Ground Penetrating Radar (GPR) to Predict Log Moisture Content of Commercially Important Canadian Softwoods

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