Nondestructive evaluation of drying stress level on wood surface using near-infrared spectroscopy

A nondestructive technique for swiftly measuring the stress level of the surface of wood is proposed, which is important for process control in timber drying. Partial least squares (PLS) regression models for predicting surface-released strain (ε) were developed using NIR spectra obtained from Sugi (Cryptomeria japonica D. Don) samples during drying. The predictive ability of the models was evaluated by PLS analysis and by comparing NIR-predicted ε with laboratory-measured values. The PLS regression model using the NIR spectra pre-processed by MSC and second derivatives with a wavelength range of 2,000–2,220 nm showed good agreement with the measurement (R ² = 0.72). PLS analysis identified the wavelengths around 2,035 nm as making significant contributions to the prediction of ε. Orthogonal signal correction (OSC) was an effective pre-processing technique to reduce the number of factors required for the model using the wavelength range 1,300–2,500 nm. However, the predictive ability of the OSC-corrected model was not improved. Elapsed times to reach the maximum tensile stress (T _max) and the stress reversal point (T _rev) at the wood surface during drying were detected correctly for 75 % of the samples. The results show that NIR spectroscopy has potential to predict the drying stress level of the timber surface and to detect critical periods in drying, such as T _max and T _rev.     

Separating Norway spruce heartwood and sapwood in dried condition with near-infrared spectroscopy and multivariate data analysis

Norway spruce [Picea abies (L.) Karst.] heartwood and sapwood have differing wood properties, but are similar in appearance. An investigation was made to see whether near-infrared spectroscopy (NIRS) could be used with multivariate statistics for separation between heartwood and sapwood in dry state on tangential longitudinal surfaces. For classification of wood into sapwood and heartwood, partial least square (PLS) regression was used. Orthogonal signal correction (OSC) filtering was used on the spectra. This study shows that a separation of sapwood and heartwood of spruce is possible with NIR spectra measured in a laboratory environment. The visible-wavelength spectra have significant influence on the predictive power of separation models between sapwood and heartwood of spruce. All 44 specimens in the calibration set were correctly classified into heartwood and sapwood. Validation of the model was done with a prediction set of 16 specimens, of which one was classified incorrectly.     

Eucalyptus camaldulensis density and fiber length estimated by near-infrared spectroscopy

Density and fiber length belong to the parameters that are used by the pulping industry as indicators of wood quality for different industrial processes and final paper products. The feasibility of Fourier transform near-infrared (FT-NIR) spectroscopy for the non-destructive evaluation of fiber length and air-dry density of fast-growing E. camaldulensis from Thailand was investigated using 50 samples. NIR spectra taken from solid wood and air-dry density as well as fiber length were used for partial least squares (PLS) regression analyses. It is the first time that the fiber length of E. camaldulensis solid wood could be predicted with high accuracy and precision and that the ratios of performance to deviation (RPD) obtained are the first that fully fulfill the requirements of AACC Method 39-00 (AACC 1999) for screening in breeding programs (RPD?≥?2.5). The RPDs for cross-validation (test set validation) of the NIR-PLS-R models of 3.3 (3.8) for air-dry density and 3.5 (3.9) for fiber length allow drawing the conclusion that the models are at least applicable for screening in breeding programs as they lie in-between screening (RPD?≥?2.5) and quality control (RPD?≥?5). Even when 40% of the samples were removed in cross-validation of the air-dry density model, the RPD is 3.2, which confirms that the model is robust, stable, and well qualified for prediction. The good model statistics obtained in this study might be due to the fact that measurement sites for the measurement of NIR spectra, air-dry density, and fiber lengths were strictly coincided.     

NIR PLSR model selection for Kappa number prediction of maritime pine Kraft pulps

A total of 910 maritime pine (Pinus pinaster Aiton) wood discs, belonging to a genetic trial of 80 families with 11–12 trees per family, were used in this study. A near infrared (NIR) partial least squares regression (PLSR) model for the prediction of Kappa number of Pinus pinaster Aiton pulps obtained from samples pulped under identical conditions was calculated. Very good correlations between NIR spectra of maritime pine pulps and Kappa numbers in the range from 58 to 100 were obtained. Besides the raw spectra, spectra pre-processed with ten methods were used for PLS analysis (cross validation with 57 samples), showing that even after test set validation (with 34 samples) no model decision could be made due to almost identical statistics. The final evaluation that proved the predictive power of the models by predicting pulps with unknown Kappa numbers allowed choosing a model according to a minimal number of outliers found during this process. The minimum–maximum normalized spectra in the wave number range from 6,110 to 5,440 cm⁻¹ used for the calculation gave the best model with a root mean square error of prediction of 2.3 units of Kappa number, a coefficient of determination of 95.9%, and one PLS component. The percentage of outliers during evaluation was 0.9%.     

Feasibility of near-infrared spectroscopy for online multiple trait assessment of sawn lumber

Near-infrared (NIR) spectroscopy coupled with multivariate analysis was applied to estimate multiple traits of sawn lumber. The effects of the lumber conveying speed (LCS) and measurement resolution of spectra (MRS) on the calibrations were examined. NIR spectra ranging from 1300 to 2300 nm were acquired at LCSs of 10, 20, and 30 m/min and at MRSs of 2, 4, and 16 nm. Prediction models of bending strength (F _b), modulus of elasticity in bending tests (E _b), dynamic modulus of elasticity (E _fr), and wood density (DEN) were developed using partial least-squares (PLS) analysis. LCS and MRS did not significantly influence the calibration performance for any wood property. The regression coefficients also showed no clear differences for any of the conditions. This indicates that the important explanatory variables included in the models are not greatly influenced by these measurement conditions. PLS2 analysis results, when presented graphically, allowed easy interpretation of the relationships between wood mechanical properties and chemical components, e.g., bending strength and stiffness were mainly related to polysaccharides cellulose and hemicellulose. NIR spectroscopy has considerable potential for online grading of sawn lumber, despite the harsh measurement conditions.     

桉树材性性状近红外预测模型的建立

试验以采集的100份桉树不同组合杂交子代的木芯及木粉样品作为研究对象,以常规方法测定所取木材样品的木材密度、纤维长度和纤维宽度并用 Polychromix 手持式近红外仪采集了自然风干状态木粉的近红外光谱信息。光谱数据的处理及建模用 Unscrambler 9.7软件完成。建模结果显示：木材密度、纤维长度和纤维宽度的预测精度均可达90%以上。建模过程中,木材密度较纤维长度和纤维宽度所需的校正样本集数量要多,说明要达到一定的预测精度,纤维长度和纤维宽度其所建模型的预测范围会相应变小。     

Quantifying lignin and holocellulose content in coniferous decayed wood using near-infrared reflectance spectroscopy

To determine the independent decomposition rates of lignin and cellulose of decayed woody debris, a technique for the rapid analysis of lignin and cellulose is required. We applied a near-infrared spectroscopy (NIRS) technique to measure the lignin and holocellulose content in decayed wood. We succeeded in creating partial least-squares (PLS) models to estimate the lignin and holocellulose content in the decayed wood of five species using NIR spectra. Although the accuracy was acceptable for the estimation of a five-species mixed model (R ² = 0.970 for lignin and R ² = 0.962 for holocellulose), it was further improved when the model was applied to each species independently. This combination of NIRS and a PLS model is a valuable tool for the determination of the lignin and holocellulose content in decayed wood. The technique is time efficient (3 min per sample) and non-hazardous (no acid treatment is required).     

Rapid prediction of wood crystallinity in <Emphasis Type="Italic">Pinus elliotii</Emphasis> plantation wood by near-infrared spectroscopy

Crystallinity is an important property of woody materials; it responds to tree growth traits, structure, and chemical composition, and has a significant effect on Young’s modulus, dimensional stability, density, and hardness, etc. The ability of near-infrared (NIR) spectroscopy coupled with multivariate analysis to rapidly predict the crystallinity of slash pine (Pinus elliotii) plantation wood was investigated. The results showed that the NIR data could be correlated with the X-ray diffraction (XRD)-determined crystallinity of slash pine wood by use of partial least squares (PLS) regression, producing excellent coefficients of determination, r ², and root mean square error of calibration, RMSEC. The use of either reduced spectral ranges or the selection of certain wavelengths consistent with known chemical absorptions did not have any detrimental effect on the quality of PLS models allowing the use of inexpensive, small, and portable spectrometers. These studies show that NIR spectroscopy can be used to rapidly predict the crystallinity of slash pine wood.     

Nondestructive estimation of wood chemical composition of sections of radial wood strips by diffuse reflectance near infrared spectroscopy

The use of calibrated near infrared (NIR) spectroscopy for predicting the chemical composition of Pinus taeda L. (loblolly pine) wood samples is investigated. Seventeen P. taeda radial strips, representing seven different sites were selected and NIR spectra were obtained from the radial longitudinal face of each strip. The spectra were obtained in 12.5 mm sections from pre-determined positions that represented juvenile wood (close to pith), transition wood (zone between juvenile and mature wood), and mature wood (close to bark). For these sections, cellulose, hemicellulose, lignin (acid soluble and insoluble), arabinan, galactan, glucan, mannan, and xylan contents were determined by standard analytical chemistry methods. Calibrations were developed for each chemical constituent using the NIR spectra, wood chemistry data and partial least squares (PLS) regression. Relationships were variable with the best results being obtained for cellulose, glucan, xylan, mannan, and lignin. Prediction errors were high and may be a consequence of the diverse origins of the samples in the test set. Further research with a larger number of samples is required to determine if prediction errors can be reduced.     

Prediction of mechanical properties of Chinese fir wood by near infrared spectroscopy

Mechanical properties and the visible and near infrared (NIR) (350–2500 nm) spectra obtained from longitudinal and transverse face of 155 small clear wood samples of Chinese fir (Cunninghamia lanceolata) were measured, and 103 of them were used to establish calibration models. Calibrations were tested on an independent set (52 samples). Differences between calibrations developed by using the longitudinal and transverse face were small. The calibrations developed by using NIR spectra (350–2500 nm) collected from transverse face were slightly inferior to those developed by using NIR spectra collected from longitudinal face. When reducing the spectral range to between 780 and 1050 nm, the calibrations developed by using NIR spectra collected from longitudinal face were slightly inferior to those developed by using NIR spectra collected from transverse face, and reducing the spectral range causes no decrease in the quality of the models developed using NIR spectra collected from transverse face. Partial lease square (PLS) modeling and test showed that calibrations developed using the visible and NIR spectra from transverse and longitudinal faces and calibrations developed by using the reducing spectral range (780–1050 nm) from the transverse face were moderate, and have a RPD range from 1.51 to 1.90. It is concluded that NIR spectroscopy can be used as an initial screening. __________ Translated from Journal of Northwest Forestry University, 2007, 22(5): 149–154 [译自: 西北林学院学报]     

Use of Near Infrared Spectroscopy to Measure Mechanical Properties of Solid Wood

The visible and near infrared （NIR） （350-2500 nm） spectra and the MOE of 438 small clear wood samples from Chinese fir, eucalyptus and poplar 72 were measured. Using partial least-square （PLS） modeling, the NIR spectra could be used to predict MOE and MOR of the clear-wood samples from Chinese fir and eucalyptus solid wood. NIR spectra could only be used to Predict MOE but not MOR of poplar clear-wood samples. With the exception of MoR of poplar clear-wood samples, the correlations between NIR and the mechanical properties are very strong, and the calibration and test correlation coefficients are both above 0.80.     

The use of near-infrared scanning for the prediction of pulp yield and chemical properties of Pinus patula in the Mpumalanga escarpment area of South Africa

Near-infrared (NIR) scanning technology is regarded as a potential tool for rapid determination of wood properties, which can substitute time-consuming and costly traditional methods. Pinus patula is the most important softwood species in South Africa, and this study is aimed at developing NIR calibration models for quick prediction of its pulp yield and chemical composition. A total of 85 trees from 17 plots, covering the range of site conditions in the Mpumalanga escarpment area, were sampled. Two samples were taken from each tree: a 1 m billet above breast height and a 20 mm disc at breast height. The billet was pulped using the kraft pulping process to determine pulp yield. The disc was ground into sawdust and the chemical composition was determined using conventional wet chemistry. Sawdust was scanned on a NIR spectrophotometer to produce NIR spectra. Calibration models to predict pulp yield, cellulose and lignin content were developed by applying chemometrics and partial least squares regression. Validation and determination of prediction accuracy of the models were performed using independent data. The prediction of cellulose and lignin were acceptable with correlations of determinations (r ²) of 0.71 and 0.70 respectively. Standard errors of prediction were generally low (less that 0.86) for all the models. The prediction r ² for both total and screened pulp yield were only 0.62. Although the cellulose and lignin models can be used with confidence, the expansion of the sample size for follow-up research must be considered in order to increase the variability of tested wood properties and improve the prediction strength of the models. The NIR calibration provided in this study can contribute to the efficient examination of forest site-to-wood quality relationships that would enhance precision forest management and wood processing efficiency.     

Discrimination of viable and empty seeds of Pinus patula Schiede & Deppe with near-infrared spectroscopy

Sustainable forest production demands a continuous supply of high quality seeds for the production of seedlings in the nursery or for direct sowing. Here, we demonstrated the potential of near infrared spectroscopy as a rapid technique to discriminate viable and empty seeds of Pinus patula Schiede & Deppe. Near infrared spectra were collected from single seeds in transmittance and reflectance modes. To discriminate viable and empty seeds, multivariate classification models were developed with partial least squares (PLS) regression using the digitized spectra as a regressor and a y-vector of artificial values (1 for viable and –1 for empty seeds) as a regressand. Viable and empty seeds were perfectly distinguished by PLS models computed on full and selected transmittance spectroscopy data, while those derived from full NIR reflectance spectra recognized 96 % of viable and 88 % of empty seeds. Analyses made on selected NIR reflectance spectra improved the classification rate of empty seeds to 100%. Difference spectra and PLS weights indicated that the origin of spectral differences between viable and empty seeds was attributed to differences in fatty acids and proteins that were totally absent in empty seeds. The result shows the prospect of developing rapid filter-based sorting equipment that can easily be automated.     

近红外光谱法快速测定速生桉木化学成分含量

用常规方法测定了104个速生桉木样品的综纤维素、聚戊糖、酸不溶木素及苯醇抽出物含量并采集了样品的近红外光谱。对原始光谱进行多元散射校正后,运用偏最小二乘法和交互验证的方法,确定最佳主成分数并建立样品相关化学成分含量的校正模型。独立验证中综纤维素、聚戊糖、酸不溶木素和苯醇抽出物模型的决定系数 Rval2分别为0.9067、0.9033、0.9504、0.9570;预测均方根误差(RMSEP)分别为0.33%、0.50%、0.31%、0.17%;相对分析误差(RPD)值分别为3.22、3.20、4.43、4.73;绝对偏差(AD)分别为?0.53%~0.60%、?0.95%~0.77%、?0.55%~0.52%、?0.22%~0.29%,4个校正模型较好地预测了验证集样品的化学成分含量,基本满足制浆造纸工业中快速测定速生桉木原料的需求。     

Use of near infrared spectroscopy to measure the chemical and mechanical properties of solid wood

Near infrared (NIR) spectroscopy (500 nm–2400 nm), coupled with multivariate analytic (MVA) statistical techniques, have been used to predict the chemical and mechanical properties of solid loblolly pine wood. The samples were selected from different radial locations and heights of three loblolly pine trees grown in Arkansas. The chemical composition and mechanical properties were measured with traditional wet chemical techniques and three point bending tests, respectively. The microfibril angle was measured with x-ray scattering. These chemical and mechanical properties were correlated with the NIR spectra using projection to latent structures (PLS) models. The correlations were very strong, with the correlation coefficients generally above 0.80. The mechanical properties could also be predicted using a reduced spectral range (650 nm–1150 nm) that should allow for field measurements of these properties using handheld NIR spectrometers.     

Rapid spectroscopic separation of three Canadian softwoods

Near Infrared (NIR) and Fluorescence (FS) spectroscopy were investigated for their ability to rapidly separate three Canadian softwoods: balsam fir, western hemlock, and white spruce. NIR and FS spectral data were used to develop classification models using soft independent modeling of class analogies (SIMCA) method. For each wood species, spectra of 90 wood specimens were collected over a wavelength window of 800–2,500?nm for NIR spectral data and a wavelength range of 380–540 and 380–705?nm for FS spectral data. Raw spectra and first-derivative-transformed spectra were used to develop NIR calibration models to separate the three wood species using the wavelength ranges, 800–2,500, 1,100–2,200, and 1,300–2,000?nm, by the SIMCA method. Similarly, FS raw spectral data were also used to develop FS calibrations using wavelength ranges of 380–540 and 380–705?nm. Principal component analysis models were made for each class from the calibration set consisting of 65 specimens of each of the three wood species. Specimens not present in the calibration set (27 specimens of each wood species) were tested for classification according to the SIMCA method at a 5 and 25% significance level. Type I error associated with the models developed with NIR spectral data ranged from 0 to 19 and 0 to 52% for the 5 and 25% significance levels, respectively, while type II error ranged from 2 to 50 and 0 to 19%, respectively. When tested at a 5% significance level, there was no significant improvement in NIR models developed with first-derivative-transformed spectra over models developed with raw spectra. Type I error associated with the models developed with Fluorescence spectral data ranged from 0 to 4 and 7 to 30% for the 5 and 25% significance levels, respectively, while type II error ranged from 1 to 9 and 0 to 1%, respectively. There were no significant differences in performance of FS models developed with spectra using wavelength ranges of 380–540 and 380–705?nm.     

Nondestructive determination of paper properties of Populus × euramericana with near infrared spectroscopy

Five Populus x euramericana wood samples representing three different sites were selected and nearinfrared （NIR） spectra were obtained. For these sections, basis weight, brightness and three mechanical properties （tensile index, tearing index and bursting index） were determined by standard analytical methods. Calibrations were developed for each paper property using the NIR spectra, data on paper properties, using partial least squares （PLS） regression. The results show that the coefficients of correlation of calibration and validation for basis weight were 0.8824 and 0.8299, respectively; the standard error of calibration （SEC） and prediction （SEP） were 1.150 and 1.170, respectively. In testing for brightness, the correlation coefficient of calibration was 0.9621 and for validation 0.9612, while the SEC and SEP were 0.997 and 1.300, respectively; paper brightness and NIR spectroscopy were highly correlated. NIR spectroscopy can be used to predict tensile, tearing and bursting indices of paper samples rapidly. We found that the paper properties fitted by NIR and GB methods were highly correlated. The coefficients of correlation of calibration and validation for basis weight exceeded 0.8000, while the SEC and SEP were very small. These results reveal that the five paper properties of Populus x euramericana and those predicted by the NIR model were highly correlated. We conclude that the NIR models can be used for the prediction of paper properties.     

Determination of physical and chemical properties and degradation of archeological Japanese cypress wood from the Tohyamago area using near-infrared spectroscopy

Here, we evaluated the application of near-infrared (NIR) spectroscopy for estimating the degradation level of archeological wood samples from the Tohyamago area, the dendrochronological ages of which were also determined. The wood samples were radially cut from three logs obtained from the Tohyamago area. NIR reflectance spectra were measured from the tangential faces of air- and oven-dried wood samples using a Fourier transform NIR spectrophotometer. The second derivative spectra within the wavenumber range of 6400–5200 cm^?1, in which the effect of moisture content in wood is suspected to be insignificant, showed a characteristic behavior with age. By comparing the second derivative spectral change in our wood samples with that in wood degraded by aging, thermal treatment, fungal attack, and lightning reported in the literature, we found that the second derivative spectra of wood samples from one log was similar to those of wood degraded by hygro-thermal treatment, whereas those of wood samples from another log was similar to those of wood degraded by brown-rot fungi. The physical and chemical properties of archeological wood were well predicted using a combination of partial least square regression analysis and NIR spectroscopy.     

Non-destructive assessment of <Emphasis Type="Italic">Pinus</Emphasis> spp. wafers subjected to <Emphasis Type="Italic">Gloeophyllum trabeum</Emphasis> in soil block decay tests by diffuse reflectance near infrared spectroscopy

The use of calibrated near infrared (NIR) spectroscopy for measuring and predicting the advancement of wood decay in Pinus spp. sapwood wafers that were subjected to Gloeophyllum trabeum for periods ranging from 1 to 10 days was investigated. NIR spectra were obtained from the center of the cross-sectional face of each sample before and after decay tests. Mass loss and compression tests were also used to measure the progression of decay. Calibrations were created from NIR spectra, mass loss, and compression strength data using untreated and mathematically treated (multiplicative scatter correction and first and second derivative) spectra. Strong relationships were derived from the calibrations with the strongest R ² values being 0.98 (mass loss) and 0.97 (compression strength). Calibrations for mass loss showed the strongest statistics for predicting wood decay of a separate test set (0.85 raw, second derivative to 0.76 multiplicative scatter correction (MSC), while predictions for compression strength of the decayed samples resulted in R ² of 0.69 (raw) to 0.54 (MSC). Calibrations created from the amount of time the samples were decayed showed strong statistics, indicating that NIR spectroscopy can predict the early stages of wood decay.     

Near-infrared spectroscopy prediction of southern pine No. 2 lumber physical and mechanical properties

This study investigated near-infrared spectroscopy (NIRS) to rapidly estimate physical and mechanical properties of No. 2 2 × 4 southern pine lumber. A total of 718 lumber samples were acquired from six mills across the Southeast and destructively tested in bending. From each piece of lumber, a 25-mm-length block was cut and diffuse reflectance NIR spectra were collected from the transverse face using a FOSS 5000 scanning spectrometer. Calibrations were created using partial least squares (PLS) regression and their performance checked with a prediction set. Overall moderate predictive ability was found between NIRS and the properties for the calibration and prediction sets: block specific gravity (SG) (R ² = 0.66 and R _p ²  = 0.63), lumber SG (0.54 and 0.53), modulus of elasticity (MOE) (0.54 and 0.58), and modulus of rupture (MOR) (0.5 and 0.4). Model performance for MOE (R _p ²  = 0.70) and MOR (R _p ²  = 0.50) improved when performing PLS regression on a matrix containing lumber SG and NIR spectra. Overall NIRS predicted MOE better than linear models using lumber SG (R ² = 0.46), whereas lumber SG (R ² = 0.51) predicted MOR better than NIRS. Overall NIRS has reasonably good predictive ability considering the small volume of wood that is scanned with the instrument.