Prediction of Yellow-Poplar (Liriodendron Tulipifera) Veneer Stiffness and Bulk Density Using near Infrared Spectroscopy and Multivariate Calibration

Abstract

This study investigated the feasibility of using near infrared (NIR) spectroscopy and multivariate calibration to predict bulk density and stiffness of 3.2 mm thick yellow poplar veneer strips. Full-range (800–2500 nm) raw NIR spectra and spectra pre-treated using the first derivative method, along with spectra from three other different wavelength windows of 1200–2400 nm, 1800–2400 nm and 1400–2000 nm were regressed against the bulk density (kg m−3) values and the dynamic modulus of elasticity (stiffness; GPa) of the veneers using the projection to latent structures (PLS) method to develop calibration models. All predictive models developed performed well in the prediction of bulk density and stiffness of new test samples that were not included in the calibration models. R2 values ranged from 0.67-0.78 and 0.56-0.72, respectively, for bulk density and stiffness. There was significant improvement in models developed with first derivative spectra over models developed with raw spectra. The models developed using the first derivative used fewer latent variables to achieve predictive models with higher R2 values, lower root mean square errors of prediction (RMSEP) and standard errors of prediction (SEP). Models developed using the full NIR spectral range (800–2500 nm) and the NIR spectral region of 1200–2400 nm performed better than models developed using the restricted NIR wavelength regions of 1800–2400 nm and 1400–2000 nm. However, there was no clear distinction between models developed using the full NIR spectral range and the NIR spectral region of 1200–2400 nm. Overall, models developed with the first derivative pre-processed spectra using the whole NIR spectrum performed best in predictability. The results of this study show the potential of using multivariate data analysis coupled with NIR spectroscopy for on-line sorting and assessment of veneer stiffness prior to the lay-up process in the manufacturing of veneer-based engineered wood products such as plywood, Parallam and laminated veneer lumber.

Estimation of physical and mechanical properties of agro-based particleboards by near infrared spectroscopy

“…Over the last years, the association of near infrared spectroscopy and multivariate analysis has been demonstrated as a rapid and reliable tool for characterizing engineering biomaterials products (Meder et al 2002;Rials et al 2002;Dolezel-Horwath et al 2005;Kelley et al 2005;Kent et al 2006;Adedipe and Dawson-Andoh 2008;Campos et al 2009;Hein et al 2009a). Although it is well known that both spectral noise and reference method affect the accuracy and the precision of NIR predicted values (Rodrigues et al 2006), in this study, different samples (cut from the same board) were used for NIR spectra acquisition and for determination of the reference data.…”

A review of near-infrared spectroscopy for monitoring moisture content and density of solid wood

“…2 (Thygesen and Lundqvist 2000a, Via et al 2003, Adedipe and Dawson-Andoh 2008a. Increase in density also produces a baseline shift in absorbance ( Fig.…”

“…For example, when limiting the spectral range to 1400 nm to 1940 nm, Adedipe and Dawson-Andoh (2008a) could predict wood moisture content with almost the same accuracy as when they used a larger spectral range (800-2500 nm). For the prediction of wood density, it seems that reducing this range either to 1800-2400 nm or 1400-2000 nm and therefore omitting information about cellulose and hemicellulose peaks reduces the quality of the model (Adedipe and Dawson-Andoh 2008b). However, Table 2 shows that sufficient accuracy can still be achieved when using reduced spectral range to sort wood products into broad density classes.…”

Challenges in the use of Near Infrared Spectroscopy for improving wood quality: A review

“…Moisture content (MC) exchange between wood and air depends on the relative humidity and temperature of the air and the current amount of water in the wood (Glass & Zelinka, 2010). The moisture variation has an important influence on wood properties and performance, affecting strength, drying, processing, glue curing, and bond performance (Adedipe & Dawson-Andoh, 2008). As pointed out by Leblon et al (2013), wood industry has particular interest for models capable of handling a broader range of moisture content variations in order to be able to measure MC at different steps of the manufacturing process, whatever the level of MC.…”

“…They obtained good predictions using NIR spectra collected from transverse or radial surfaces between 1000 and 2300 nm, with root mean square of errors of prediction of less than 3.6%. Adedipe & Dawson-Andoh (2008) have examined the feasibility of using NIR spectra for estimating MC of yellow-poplar (Liriodendron tulipifera) veneer sheets. According to their results, both principal component regression (PCR) and partial least squares regression (PLS) models for estimating MC presented correlations R² > 0.94.…”