Rapid and nondestructive evaluations of wood mechanical properties by near infrared spectroscopy

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1 Rapid and nondestructive evaluations of wood mechanical properties by near infrared spectroscopy Takaaki Fujimoto Researcher of Wood anatomy and Physics Laboratory Hokkaido Forest Products Research Institute Asahikawa, Hokkaido, , Japan Kazushige Matsumoto, Researcher, Hokkaido Forest Products Research Institute Yohei Kurata, postgraduate, Graduate School of Bioagricultural Sciences, Nagoya University Satoru Tsuchikawa, Professor, Graduate School of Bioagricultural Sciences, Nagoya University Summary Near infrared (NIR) spectroscopy, coupled with multivariate analytic statistical techniques, has been used to predict the mechanical properties of solid wood samples taken from small clear and full length lumber specimens of hybrid larch. The specific mechanical characteristics evaluated were the modulus of elasticity (MOE), the modulus of rupture (MOR), compression strength parallel to the grain (CS) in small clear specimens, dynamic modulus of elasticity of air-dried lumbers (E fr ), and wood density (DEN). Partial least squares (PLS) regression calibrations were developed for each wood property and found that the calibrations gave relatively strong relationships between laboratory-measured and NIR-predicted values in small clear specimens, with coefficients of determination ranging from 0.61 to The calibration models were applied to the prediction data sets (unknown samples), suggesting that NIR spectroscopy has the potential to predict the mechanical properties with adequate accuracy. Comparison of the prediction based on spectra obtained from the radial vs tangential face showed that the prediction models based on spectra obtained from radial face were slightly superior. Although the accuracy of calibration and prediction was lesser than the small clear specimens, reasonable predictive model for wood stiffness could be taken from the full length lumber specimens. Regression coefficients resulting from PLS analysis indicates that variation in wood components such as cellulose, lignin and possibly hemicellulose governs wood mechanical properties. 1. Introduction Rapid cost-effective methods for measuring wood properties are required to supply the useful information for the final products since these properties are highly variable among species and individuals, and even in the same stem. It is considered that this general lack of uniformity of wood properties as one of the greatest problems facing the wood industry. Traditional methods employed to measure important wood properties are time consuming, expensive, and often destructive. Then, wide varieties of techniques for nondestructive testing for wood, such as mechanical, electromagnetic, nuclear, and acoustics including ultrasonics and vibration way, have been introduced in wood industry [1]. Near-infrared (NIR) spectroscopy, a fast growing technique for nondestructively evaluating organic materials, has found widespread use in variety of industries including food and agriculture, pharmaceutical, and petroleum [2]. More recent studies have also demonstrated that NIR spectroscopy could be successfully applied to the rapid analysis of chemical and physical wood characteristics [3, 4] and this technique appears high potential on-line or at-line using for quality control of, or segregation of pulp and paper or solid wood. Although the potential of NIR spectroscopy as a nondestructive measurement technique has been shown for many wood properties, the theoretical possibility of estimating other properties with high accuracy should be further considered [5]. A number of studies have been reported with regard to the evaluation of wood mechanical properties by NIR spectroscopy [6, 7]. However, most of these studies have dealt with the small clear specimens. The NIR technique by diffuse-reflectance spectra is confined to the evaluation of

2 surface layers for specimens [2]. It is, therefore, essential to examine whether the NIR method can evaluate the mechanical properties for the full length lumber with large dimensions. The objectives of the present study are to evaluate the potential of nondestructive estimation of wood mechanical properties by NIR spectroscopy and elucidate the relationship between these mechanical properties and the corresponding NIR spectra. Two sample set, i.e., small clear wood and full length lumber specimens, were prepared for analysis. Vibrational spectroscopic background was examined to clarify the applicability of NIR technique to nondestructive measurement of mechanical properties of wood. 2. Experimental 2.1 Sample preparation and measurement of mechanical properties Wood samples were collected from two progeny test stand of hybrid larch (Larix gmelinii var. japonica Larix kaempferi) located in Hokkaido, northern Japan. The stand ages of each plantation ranged from 29 to 33 years old and the total number of sample tree were about 300. The average height and diameter were 20.6 m ( m) and 21.6 cm ( cm), respectively. After felling, first and second logs, 1.8 m in length, were obtained from each tree and transported to the Hokkaido Forest Products Research Institute sawmill. The small clear specimens were obtained from the first logs. A board, 40 mm in thickness, was sawn from each log centered on the pith. The board was dried using a conventional kiln-drying schedule for wooden materials; then the stakes were successively processed. Small clear specimens, as free from knots as possible, were cut from the stakes for static bending tests with a size of mm and for compression tests with a size of mm. All specimens were conditioned to equilibrium in a room kept at 20 ºC and 65% relative humidity. The mechanical properties of the small clear specimens were tested using an Instron universal testing machine. All testing procedures employed conformed to those of the Japan Industrial Standards Z 2101 [8]. The specific mechanical characteristics evaluated were the modulus of elasticity (MOE) and modulus of rupture (MOR) in the bending test, the maximum crushing strength in compression parallel to grain (CS), and the wood density (DEN_C). Both DEN_C and moisture content were measured using a small, undamaged block, which was obtained from near the rupture area after the bending test. The average moisture content of the small clear specimens was 13.1%. These blocks were used for near infrared measurements. The lumber specimens with full-size were obtained from second logs. Sample lumbers which were supposed to use for the glued laminated timber were successively sawn from the bark side of each log, with a cross-sectional dimension of mm. All lumbers were kiln-dried through the same schedule as mentioned above and then were processed into final size of mm by a moulder. The dynamic modulus of elasticity of the lumbers (E fr ) was measured using the longitudinal vibration method [9]. The weight, length, and dimensions of lumbers were measured to calculate the wood density (DEN_L). 2.2 NIR measurements Near-infrared diffuse-reflectance spectra were acquired on a MATRIX-F spectrophotometer (Bruker Optics K.K.) equipped with the contact-free fiberoptic NIR illumination and detection head containing 4 tungsten light sources. The scattered light is collected and guided via a fiber optic cable to the spectrometer, where a contactless measurement can be performed remotely. The NIR spectra were obtained at 8 cm -1 intervals over the wavenumber range 7700 to 4300 cm -1. Sixteen scans were collected and averaged into a single average spectrum; this resulted in an acquisition time of 30 sec approximately. The working distance was 17 cm. The measurements were performed on the radial and tangential face after completion of the bending test using a small, undamaged block, as described above. In the case of lumber specimens, spectra were obtained from both side of tangential face near the center of samples and the average value for each of the two measurements was used for the following analyses.

3 2.3 Statistical analysis The spectral data from the small clear and full length lumber specimens were labeled SAMPLE_A and SAMPLE_B, respectively. Furthermore, the 220 spectral data from the SAMPLE_A were split randomly into the calibration and prediction sets, which consisted of 150 and 70 samples. In the case of SAMPLE_B, the data from 100 samples were also split randomly into the calibration and prediction sets, which consisted of 80 and 20 samples, respectively. Sample set conditions are summarized in Table 1. The specimens were taken from both juvenile and mature wood; therefore, the wide ranges of data were obtained in any case. Table 1 Summary of wood properties for calibration and prediction sets. Calibration set Prediction set Wood property* Mean Min Max SD Mean Min Max SD SAMPLE_A (Clear wood)** MOE (GPa) MOR (MPa) CS (MPa) DEN_C (g/cm 3 ) SAMPLE_B (Lumber) E fr (GPa) DEN_L (g/cm 3 ) * MOE: modulus of elasticity; MOR: modulus of rupture; CS: crushing strength in compression parallel to grain; DEN_C: airdried wood density of clear wood; E fr : dynamic modulus of elasticity of air-dried umber; DEN_L: air-dried wood density of lumber. ** SAMPLE_A: Sample set from small clear wood tests, containing 150 and 70 specimens for calibration and prediction; SAMPLE_B: Sample set from full length lumber, containing 80 and 20 specimens for calibration and prediction. Multivariate analysis was performed using the Unscrambler version9.6 (CAMO AS, Norway) software. Methods used for preliminary examination of the data included smoothing the spectral data and second-derivative transformation. Calibrations were developed for each wood property by partial least squares (PLS) regression analysis [10]. The final number of factors selected for incorporation into the model was chosen to minimise the residual variance when using full crossvalidation. Calibration and validation statistics for each regression included the coefficient of determination (R 2 ), the standard error of calibration (SEC), the standard error of cross-validation (SECV), and the standard error of prediction (SEP), respectively. The ratio of performance to deviation (RPD), calculated as the ratio of the standard deviation of the reference data to the SEP, was used to determine the predictive ability of the calibrations [11]. Determination of RPD allows comparison of calibrations developed for different wood properties that have differing data ranges and units; the higher the RPD the more accurately the data fitted by the calibration. 3. Results and Discussion 3.1 Partial Least Squares Analysis for Each Property Partial least squares regression calibrations were found for each wood property of SAMPLE_A and SAMPLE_B, respectively. Statistical results are summarized in Table 2. Fig. 1 shows the relationships between laboratory-measured and NIR-predicted values for MOE, MOR, and CS of radial face. In the case of SAMPLE_A, the calibrations gave relatively strong relationships between measured and NIR-predicted values with coefficients of determination ranging from 0.61 to The calibration equations were applied to the prediction set of 70 specimens. Predictions of the respective wood properties were also good, where coefficients of determination ranged from 0.68 to The SECV to be closer to the true error of a calibration was nearly identical to the SEP in all cases. The calibrations presented in this study demonstrate that NIR spectroscopy has the potential to predict the various mechanical properties of wood samples with considerable accuracy.

4 Table 2 NIR calibrations for each wood property.* Calibration set Prediction set Wood property Factors R 2 SEC SECV 2 R P SEP RPD SAMPLE_A (Clear wood) Radial face MOE (GPa) MOR (MPa) CS (MPa) DEN_C (g/cm 3 ) Tangential face MOE (GPa) MOR (MPa) CS (MPa) DEN_C (g/cm 3 ) SAMPLE_B (Lumber) E fr (GPa) DEN_L (g/cm 3 ) * R 2 : Coefficient of determination for calibration set; SEC: standard error of calibration; SECV: standard error of crossvalidation; R 2 P : coefficient of determination for prediction set; SEP: standard error of prediction; RPD: ratio of performance to standard deviation. Since wood is a highly heterogeneous material with anisotropic properties, it is very important to consider the influence of measuring direction. The NIR spectra of all samples were acquired from both radial and tangential face. Comparison of the PLS parameters for the radial vs tangential face showed that the models based on spectra obtained from radial face were slightly superior. This results the models having higher correlation and RPD, while requiring fewer factors (Table 2). Since both earlywood and latewood were found on the radial face, the resulting spectrum might be better representation of the total wood characteristics than a spectrum of the tangential face which earlywood and latewood were found irregularly [6]. This argument is consistent with the results of the DEN_C (Table 2).

5 In the case of SAMPLE_B, calibrations developed for E fr gave predictive relationship between measured and NIR-predicted values, although the accuracy of calibration and prediction slightly reduced (Table 2). Less-precision of E fr might be due to the measuring direction as the same argument described above. NIR spectra for the lumber specimens were acquired from the tangential face. Additionally, full length lumbers contain the various kinds of defects, such as knot, spiral grain, etc., which obviously complicate the evaluation for the mechanical properties. Multi-points measurements should be necessary in order to avoid these irregular factors instead of one-point measurement in this study. 3.2 Regression coefficient Figure 2 shows the original and second-derivative NIR spectra of hybrid larch wood. Wood specific absorption bands are labeled in the figure and their assignments are summarized in Table 3. Assignments of the NIR spectrum to specific wood components have been reported by a number of authors [12-16]. Wood is a composite material consisting of three major polymers, namely cellulose, hemicellulose, and lignin. The absorption bands in the NIR region are conclusively associated with these three kinds of polymers. The absorption bands at 5219 and 5051 cm -1 (peaks 8 and 9) are assigned to a combination of OH stretching and deformation modes in water [15]. In order to consistently explain the applicability of NIR spectroscopy to evaluation of mechanical properties of wood on the basis of vibrational spectroscopic analysis, regression coefficients for each wood parameter were investigated. In a regression model equation, regression coefficients are the numerical coefficients that express the link between variation in the predictors and variation in the response, and summarize the relationship between all predictors and a given response computing for any number of components. Figure 3 show the spectral plot of regression coefficients for MOE based on the spectra from (a) radial and (b) tangential faces for instance. General trends of the regression coefficients showed the same pattern for any traits. The black line and blue line indicate the regression coefficient and the second-derivative spectrum, respectively. Important absorption bands are labeled in Fig. 3 and their assignments are also summarized in Table 3.

6 Table 3 Assignment of the representative absorption bands for hybrid larch wood.* Wavenumber Bond vibration** Remarks Reference C-H str. + C-H def. Cellulose [12] OH str. first overtone Amorphous regions in cellulose + H 2 O [13] OH str. first overtone Crystalline regions in cellulose [13] OH str. first overtone Crystalline regions in cellulose [13] CH str. first overtone Aromatic groups in lignin [14] CH str. first overtone Furanose/pyranose due to hemicellulose [14] OH str. + 2 CO str. Semi-crystalline or crystalline regions in cellulose [14] OH str. + OH def. H 2 O [15] OH str. + OH def. H 2 O [15] OH str. + CH def. Semi-crystalline or crystalline regions in cellulose [16] CH str. + CC str. Amorphous regions in cellulose [16] OH str. + OH str. Cellulose, sugar, starch [16] * Numbers in the first column correspond to those in Fig.2. ** str.: stretching; def.: deformation. In both case, highly negative regression coefficients were found at absorption bands due to OH and CH in the semi-crystalline or crystalline regions in cellulose (peaks 1, 3, 4, 7, 10, and 12), and CH in lignin (peak 5). These results explain that increase of the semi-crystalline or crystalline regions in cellulose and lignin are related to an increase of the wood stiffness. However, the increase of CH absorption bands due to hemicellulose (peak 6) was related to the decrease in MOE. These unexpected results should be further investigated. The increase of OH absorption bands due to the amorphous regions in cellulose (peaks 2 and 11) was related to the increase in MOE. In addition, there were also some high regression coefficients at peaks 2, 8 and 9 due to water absorption bands, although its trends were not uniform, showing both positive and negative values. These facts are in agreement with the previous report [17] that the characteristic peaks concerning the amorphous regions in cellulose and water are significantly related to the variations of wood density.

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