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

Zhang, Maomao; Liu, Yana; Yang, Zhong

doi:10.15376/biores.10.4.6953-6960

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…Every precaution was taken to ensure that the surface was as homogeneous as possible between samples especially regarding the surface roughness knowing its possible influence on the absorption spectra. 38 On the surface used for spectral measurements (longitudinal-radial plane) where tracheids are present in their section, the angle of wood fibres could have a small influence on the variation of absorption spectra obtained in reflectance mode. 39 The robustness of our calibration models could also be affected by variations of wood porosity, namely anatomy differences between samples, which have more influence on light scattering effects.…”

Section: Nir Spectroscopy Calibration Modelsmentioning

confidence: 99%

Prediction of Stilbene Content from Heartwood Increment Cores of Scots Pine Using near Infrared Spectroscopy Methodology

Pulkka

Segura²,

Harju

et al. 2016

Journal of Near Infrared Spectroscopy

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High-throughput and non-destructive methods for quantifying the content of the stilbene compounds of Scots pine (Pinus sylvestris L.) heartwood are needed in the breeding for decay resistance of heartwood timber. In this study, near infrared (NIR) spectroscopy calibrations were developed for a large collection of solid heartwood increment core samples in order to predict the amount of the stilbene pinosylvin (PS), its monomethyl ether (PSM) and their sum (STB). The resulting models presented quite accurate predictions in an independent validation set with R 2 v values ranging between 0.79 and 0.91. The accuracy of the models strongly depended on the chemical being calibrated, with the lowest accuracy for PS, intermediate accuracy for PSM and highest accuracy for STB. The effect of collecting one, two or more (up to five) spectra per sample on the calibration models was studied and it was found that averaging multiple spectra yielded better accuracy as it may account for the heterogeneity of wood along the increment core within and between rings. Several statistical pretreatments of the spectra were tested and an automatic selection of wavenumbers prior to calibration. Without the automatic selection of wavenumbers, a first derivative of normalised spectra yielded the best accuracies, whereas after the automatic selection of wavenumbers, no particular statistical pretreatment appeared to yield better results than any other. Finally, the automatic selection of wavenumbers slightly improved the accuracy of the models for all traits. These results demonstrate the potential of NIR spectroscopy as a high-throughput and non-destructive phenotyping technique in tree breeding for the improvement of decay resistance in heartwood timber.

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Section: Nir Spectroscopy Calibration Modelsmentioning

confidence: 99%

Prediction of Stilbene Content from Heartwood Increment Cores of Scots Pine Using near Infrared Spectroscopy Methodology

Pulkka

Segura²,

Harju

et al. 2016

Journal of Near Infrared Spectroscopy

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“…Previous studies have shown that the presentation and preparation of the samples strongly influence the performance of the models (Hein et al 2010). Zhang et al (2015) also showed that the absorption of NIR radiations by wood samples decreases with increasing surface roughness, and it could influence the performance of NIR calibration models.…”

Section: Predicted Classesmentioning

confidence: 97%

Use of a Portable Near Infrared Spectrometer for Wood Identification of Four Dalbergia Species from Madagascar

Raobelina,

Chaix,

Razafimahatratra

et al. 2023

WFS

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This study focused on the use of Near InfraRed (NIR) Spectroscopy to address the lack of tools and skills for wood identification of Dalbergia species from Madagascar. Two sample sets of 41 wood blocks and 41 wood cores belonging to four Dalbergia species (D. abrahamii, D. chlorocarpa, D. greveana, and D. pervillei) were collected in the northern and western regions of Madagascar. Sapwood and heartwood NIR spectra were measured on wood at 12% moisture content by using a portable VIAVI MicroNIR 1700 spectrometer. Four discrimination models corresponding to sapwood and heartwood of the two sample forms were developed using Partial Least Square Discriminant Analysis (PLSDA).

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“…In wood manufacturing, the surface roughness significantly impacts the sealing performance (Liu et al 2018), painting (Zhang et al 2015), surface decoration quality (Sogutlu et al 2017), adhesive usage (Rudawska et al 2016), and paint consumption (Zhu et al 2018), so it is an important index to evaluate the surface quality of wood products. Moreover, the surface roughness of wooden parts was found to directly affect the arrangement of the processing technology and the setting of the processing allowance.…”

Section: Introductionmentioning

confidence: 99%

Prediction of straight tooth milling of Scots pine wood by shank cutter based on neural net computations and regression analysis

Cao

2022

BioRes

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Regression models and a neural net approach were used to predict the cutting performance during milling of Scots pine (Pinus sylvestris L.) by shank cutter. The influence of rake angle, spindle speed, and milling depth on surface roughness of the workpiece, as well as the connection between the milling force and the surface roughness, were thoroughly considered. Four approaches were used to predict the workpiece’s surface roughness based on the experimental data: Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Support Vector Machines (SVM), and multiple linear regression. The comparative analysis of the predictive models showed that Neural Network (NN) had preferable performance for prediction of machined surface roughness, with an R2 of 0.98. The SVM had certain fluctuations and the R2 of the multiple linear regression was just 0.87, indicating that they did not fit well for prediction machined surface roughness. In summary, the effective trend of milling parameters on the machined surface roughness of Scots pine was similar to multiple nonlinear regression, and the accurate prediction by BPNN model can provide technical support for the surface roughness of the Scots Pine and enhance shank cutter performance.

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

Cited by 8 publications

References 11 publications

Prediction of Stilbene Content from Heartwood Increment Cores of Scots Pine Using near Infrared Spectroscopy Methodology

Prediction of Stilbene Content from Heartwood Increment Cores of Scots Pine Using near Infrared Spectroscopy Methodology

Use of a Portable Near Infrared Spectrometer for Wood Identification of Four Dalbergia Species from Madagascar

Prediction of straight tooth milling of Scots pine wood by shank cutter based on neural net computations and regression analysis

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