Determination of Heat-Treated Eucalyptus and Pinus Wood Properties Using Nir Spectroscopy

Ajv, Zanuncio; Hein, Paulo Ricardo Gherardi; Ag, Carvalho; Mfv, Rocha; Aco, Carneiro

doi:10.26525/jtfs2018.30.1.117125

Cited by 8 publications

(1 citation statement)

References 16 publications

(19 reference statements)

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“…It has been shown that Vis-NIR spectroscopy can be used in wood science for the prediction of wood’s physical, chemical, anatomical, and mechanical properties such as moisture content, microfibril angle, bending strength, etc. [4,5,6,7].…”

Section: Introductionmentioning

confidence: 99%

Lifting Wavelet Transform De-noising for Model Optimization of Vis-NIR Spectroscopy to Predict Wood Tracheid Length in Trees

Via

Cheng

et al. 2018

Sensors

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The data analysis of visible-near infrared (Vis-NIR) spectroscopy is critical for precise information extraction and prediction of fiber morphology. The objectives of this study were to discuss the de-noising of Vis-NIR spectra, taken from wood, to improve the prediction accuracy of tracheid length in Dahurian larch wood. Methods based on lifting wavelet transform (LWT) and local correlation maximization (LCM) algorithms were developed for optimal de-noising parameters and partial least squares (PLS) was employed as the prediction method. The results showed that: (1) The values of tracheid length in the study were generally high and had a great positive linear correlation with annual rings (R = 0.881), (2) the optimal de-noising parameters for larch wood based Vis-NIR spectra were Daubechies-2 (db2) mother wavelet with 4 decomposition levels while using a global fixed hard threshold based on LWT, and (3) the Vis-NIR model based on the optimal LWT de-noising parameters (Rc2 = 0.834, RMSEC = 0.262, RPDc = 2.454) outperformed those based on the LWT coupled with LCM algorithm (LWT-LCM) (Rc2 = 0.816, RMSEC = 0.276, RPDc = 2.331) and raw spectra (Rc2 = 0.822, RMSEC = 0.271, RPDc = 2.370). Thus, the selection of appropriate LWT de-noising parameters could aid in extracting a useful signal for better prediction accuracy of tracheid length.

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Section: Introductionmentioning

confidence: 99%

Lifting Wavelet Transform De-noising for Model Optimization of Vis-NIR Spectroscopy to Predict Wood Tracheid Length in Trees

Via

Cheng

et al. 2018

Sensors

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Modeling mass loss of biomass byNIR‐spectrometryduring the torrefaction process

et al. 2020

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Summary When biomass is thermally treated, the enrichment of carbon in the remaining “green coal” is correlated with the temperature and duration. Other properties related to the energetic properties of the torrefied biomass are closely related to chemical modifications and correlated to the material mass loss occurring during the thermal degradation. The possibility of using near infrared spectrometry has been investigated to predict the mass loss of Pinus sylvestris wood torrefied at temperatures ranging from 220°C to 300°C with durations varying from 1 minute to 10 hours. A first mass loss prediction model (NIR‐260) associated with the mean torrefaction temperature of 260°C was developed, and appeared suitable only for this temperature due to specific chemical reactions rate. A second model (NIRS‐All), using all available data was constructed and showed an accurate mass loss prediction, for both low (220°C) and high temperatures (300°C). The main differences between NIRS‐260 and NIRS‐All models are mainly attributed to the thermal modification of hemicelluloses and cellulose fractions occurred during the wood torrefaction. The results showed near infrared spectrometry combined with multivariate calibration modeling have potential utility in an industrial context as a standardized continuous method to figure out the mass loss of biomass during torrefaction by a rapid characterization. Novelty Statement The novelty concerns the use of the Near Infrared Spectrometry (NIRS) combined with multivariate calibration modeling as a standardized method for determining the mass loss biomass during torrefaction by a rapid and nondestructive characterization. A model was constructed and showed an accurate mass loss prediction, for both low (220°C) and high temperatures (300°C). Near infrared spectrometry have potential utility in an industrial context as a standardized continuous method.

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