Hikaru Kobori

A review of recent application of near infrared spectroscopy to wood science and technology

This review article introduces recent scientific and technical reports due to near infrared spectroscopy (NIRS) at wood science and technology, most of which was published between 2006 and 2013. Many researchers reported that NIR technique was useful to detect multi traits of chemical, physical, mechanical and anatomical properties of wood materials although it was widely used in a state where characteristic cellular structure was retained. However, we should be sensitive and careful for application of NIRS, when spectra coupled with chemometrics presents unexpected good results (especially, for mechanical physical and anatomical properties). The real application for on-line or at-line monitoring in wood industry is desired as next step. Basic spectroscopic research for wooden material is also progressed. It should be a powerful and meaningful analytical spectroscopic tool.

Applicability of Vis-NIR hyperspectral imaging for monitoring wood moisture content (MC)

Abstract Visible-near-infrared hyperspectral imaging was tested for its suitability for monitoring the moisture content (MC) of wood samples during natural drying. Partial least-squares regression (PLSR) prediction of MC was performed on the basis of average reflectance spectra obtained from hyperspectral images. The validation showed high prediction accuracy. The results were compared concerning the PLSR prediction of MC mapping from raw spectra and standard normal variate (SNV) treatment. SNV pretreatment leads to the best results for visualizing the MC distribution in wood. Hyperspectral imaging has a high potential for monitoring the water distribution of wood.

Prediction of Wood Density Independently of Moisture Conditions Using near Infrared Spectroscopy

Near infrared (NIR) spectra obtained from 100 Japanese larch (Larix kaempferi) wood samples containing various amounts of moisture were used to examine the effect of moisture conditions on the accuracy of predicting wood density. Partial least squares regression (PLS-R) analysis was performed to predict wood density under air dry (DEN_ar), water impregnated (DEN_wi) and oven dry (DEN_ov) conditions. The NIR spectra varied with the moisture conditions of the wood, where the characteristic absorbance bands in the vicinity of 7320 cm−1 (1366 nm), 7160 cm−1 (1400 nm) and 7000 cm−1 (1428 nm) were related to cellulose and water. The spectral differences between high- and low-density samples varied depending on the moisture conditions; high-density samples showed low absorbance values at 7160 cm−1 when wet and showed high absorbance values at 7320 cm−1 and 7000 cm−1 when dry. DEN_ar, DEN_wi and DEN_ov could be predicted using spectra collected from the corresponding moisture conditions [coefficient of determination (R2) = 0.79–0.89; standard error of prediction (SEP) = 24–26 kg m−3]. Prediction of DEN_ar and DEN_ov could also be achieved using spectra collected from various moisture conditions (R2 = 0.86–0.87, SEP=22 kg m−3). The loadings from PLS-R analysis indicated that the absorption bands in the vicinity of 7320 cm−1, 7160 cm−1 and 7000 cm−1 played an important role in predicting wood density. NIR spectroscopy has the potential to predict wood density independently of the moisture content of the sample.

Evaluation of Binding Effects in Wood Flour Board Containing Ligno-Cellulose Nanofibers

Wood-based materials are used extensively in residual construction worldwide. Most of the adhesives used in wood-based materials are derived from fossil resources, and some are not environmentally friendly. This study explores nanofiber technology as an alternative to such adhesives. Previous studies have shown that the three-dimensional binding effects of cellulose nanofiber (CNF), when mixed with wood flour, can significantly improve the physical and mechanical properties of wood flour board. In this study, ligno-cellulose nanofibers (LCNF) were fabricated by wet disk milling of wood flour. Composite boards of wood flour and LCNF were produced to investigate the binding effect(s) of LCNF. The fabrication of LCNF by disk milling was simple and effective, and its incorporation into wood flour board significantly enhanced the physical and mechanical properties of the board.

Fast online NIR technique to predict MOE and moisture content of sawn lumber

Abstract A fast online grading apparatus for sawn lumber based on near-infrared (NIR) spectroscopy has been developed. The method is based on a novel wavelength dispersive NIR spectrophotometer equipped with a diffraction grating linear sensor and high-intensity lighting. It was possible to acquire spectra from the entire surface of Hinoki cypress lumber sections traveling on a conveyor belt at a speed of 120 m min-1. Additionally, predictive models for moisture content (MC) and modulus of elasticity (MOE) under various MC conditions were developed from the NIR spectra with the aid of partial least squares regression (PLSR) analysis. Both the MC and MOE predictive models demonstrated sufficient levels of prediction accuracy for use on high-speed conveyor belts, and the results of various experiments indicate that the developed device could be applied for the online quality certification of sawn lumber in commercial sawmills.

Mapping of Leaf Water Content Using Near-Infrared Hyperspectral Imaging

In this study, near-infrared hyperspectral imaging was applied to predict the water content of golden pothos (Epipremnum aureum) leaves, after which partial least squares regression (PLSR) analysis was performed to predict their averaged water content. The resulting predictive model was then applied to each single-pixel spectra in order to construct a water content image that could be used to evaluate the models applicability to the single-pixel spectra through partial least squares score comparisons between the averaged spectra used for calibration and the single-pixel spectra. In the next phase, it was determined that a rebuilt PLSR predictive model based on the averaged spectra of an applicable pixel showed higher prediction accuracy than that of the original model. This study provides effective information about the limitations of prediction mapping and the optimization of pixel selections for better calibrations.

Discriminant Analyzing System for Wood Wastes Using a Visible—Near-Infrared Chemometric Imaging Technique

A new optical system was developed and applied to automated separation of wood wastes, using a combined technique of visible–near-infrared (Vis-NIR) imaging analysis and chemometrics. Three kinds of typical wood wastes were used, i.e., non-treated, impregnated, and plastic-film overlaid wood. The classification model based on soft independent modeling of class analogy (SIMCA) was examined using the difference luminance brightness of a sample. Our newly developed system showed a good/promising performance in separation of wood wastes, with an average rate of correct separation of 89%. Hence, it is concluded that the system is efficiently feasible for online monitoring and separation of wood wastes in recycling mills.

Prediction of dry veneer stiffness using near infrared spectra from transverse section of green log

This study examined the feasibility of near infrared spectroscopy as a novel technique for log assessment on the basis of wood property. Near infrared (NIR) spectra were obtained from the transverse section of green log and multivariate regression analysis was carried out to predict the stiffness of veneer processed from the log. The stiffness of the veneer was dynamic modulus of elasticity measured using ultrasonic method. The calibrations of veneer stiffness had moderate relationships between measured and NIR-predicted values, with regression coefficients ranging from 0.84 to 0.88. The calibration equations were applied to the test set and it was found that predictions were also well fitted, with regression coefficients ranging from 0.67 to 0.89. The results indicate that the variation of wood stiffness within the logs could be assessed using the NIR spectra from the cross-section of logs. The spectra were obtained from green condition of the log and the stiffness of veneer was measured after kiln drying. Thus, the results imply that the wood stiffness in dry condition could be predicted using the spectra collected from green logs. If the models obtained in this study put into the imaging system, the two-dimensional map of the stiffness would be visualized on the cross-section of logs. The NIR spectroscopy coupled with imaging system could compensate the weak point of the traditional methods for log assessment.

Prediction of water content in ligustrum japonicum leaf using near infrared chemometric imaging.

Leaf water content is one of the most important indicators of the plant growth status. However, the traditional measurement of leaf water content requires a destructive process. A new technique using near infrared (NIR) imaging with the aid of chemometrics was developed to predict leaf water content. This method makes it possible to evaluate the leaf water content without destruction. Ligustrum japonicum leaves were taken, and then the water content and spectroscopic images at the wavelength of 1450 nm were obtained by a vidicon camera fitted with a band-pass filter. A calibration model able to predict water content with high accuracy was found by partial least square analysis, using cumulative histograms based on the luminance brightness as the explanatory variables. This research confirmed the feasibility of NIR chemometric imaging to monitor the plant growth condition.

Reinforcement of wood flour board containing ligno-cellulose nanofiber made from recycled wood

Wood-based materials are widely used in residential construction. These materials can be made from virgin or recycled wood, and most of the materials are fabricated with chemical adhesives. Finding replacements for such chemical adhesives poses major challenges. This study explored nanofiber technology as an alternative to these adhesives. Previous studies have shown that the three-dimensional binding effects of cellulose nanofiber (CNF) and ligno-cellulose nanofiber (LCNF), when mixed with wood flour, can significantly improve the physical and mechanical properties of wood flour board. We use the word “LCNF” as the surface nanofibrillated wood flour. Previous studies have also highlighted problems that occur during compounding and board manufacturing. In this study, a reliable method was established to mix wood flour and LCNF. The method involved a compounding machine, which facilitated board manufacturing safely. Physical and mechanical properties of the resulting wood flour boards were significantly improved with the addition of LCNF, due to close binding between LCNF and wood flour particles.