Prediction of Douglas-Fir Lumber Properties: Comparison between a Benchtop Near-Infrared Spectrometer and Hyperspectral Imaging System

Near-infrared (NIR) spectroscopy and NIR hyperspectral imaging (NIR-HSI) were compared for the rapid estimation of physical and mechanical properties of No. 2 visual grade 2 × 4 (38.1 mm by 88.9 mm) Douglas-fir structural lumber. In total, 390 lumber samples were acquired from four mills in North America and destructively tested through bending. From each piece of lumber, a 25-mm length block was cut to collect diffuse reflectance NIR spectra and hyperspectral images. Calibrations for the specific gravity (SG) of both the lumber (SGlumber) and 25-mm block (SGblock) and the lumber modulus of elasticity (MOE) and modulus of rupture (MOR) were created using partial least squares (PLS) regression and their performance checked with a prediction set. The strongest calibrations were based on NIR spectra; however, the NIR-HSI data provided stronger predictions for all properties. In terms of fit statistics, SGblock gave the best results, followed by SGlumber, MOE, and MOR. The NIR-HSI SGlumber, MOE, and MOR calibrations were used to predict these properties for each pixel across the transverse surface of the scanned samples, allowing SG, MOE, and MOR variation within and among rings to be observed.

[1]  B. Auguié.  Miscellaneous Functions for "Grid" Graphics , 2015 .

[2]  R. D. Seale,et al.  Mill variation in bending strength and stiffness of in-grade Douglas-fir No. 2 2 × 4 lumber , 2013, Wood Science and Technology.

[3]  Da-Wen Sun,et al.  Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review , 2012, Critical reviews in food science and nutrition.

[4]  Thomas L. Eberhardt,et al.  Relationships between Loblolly Pine small clear specimens and Dimension Lumber Tested in Static Bending , 2016 .

[5]  Robert J. Ross,et al.  Acoustic sorting models for improved log segregation , 2013 .

[6]  J. Howard,et al.  U.S. Timber Production, Trade, Consumption, and Price Statistics , 1999 .

[7]  Pedro Melo-Pinto,et al.  Comparison between neural networks and partial least squares for intra-growth ring wood density measurement with hyperspectral imaging , 2013 .

[8]  John W. Forsman,et al.  Nondestructive evaluation techniques for assessing modulus of elasticity and stiffness of small-diameter logs , 2002 .

[9]  P. Flæte,et al.  RAPID PREDICTION OF BASIC WOOD PROPERTIES BY NEAR INFRARED SPECTROSCOPY* , 2006 .

[10]  Olle Hagman,et al.  Real-time spectral classification of compression wood inPicea abies , 1999, Journal of Wood Science.

[11]  P. Williams,et al.  Comparison of Commercial near Infrared Transmittance and Reflectance Instruments for Analysis of Whole Grains and Seeds , 1993 .

[12]  S. Tsuchikawa,et al.  Feasibility of Near-Infrared Spectroscopy for On-Line Grading of Sawn Lumber , 2010, Applied spectroscopy.

[13]  Alexis Achim,et al.  Using Acoustic Sensors to Improve the Efficiency of the Forest Value Chain in Canada: A Case Study with Laminated Veneer Lumber , 2011, Sensors.

[14]  S. Tsuchikawa,et al.  Calibration of SilviScan data of Cryptomeria japonica wood concerning density and microfibril angles with NIR hyperspectral imaging with high spatial resolution , 2017 .

[15]  Xiping Wang,et al.  Acoustic measurements on trees and logs: a review and analysis , 2013, Wood Science and Technology.

[16]  Guolan Lu,et al.  Medical hyperspectral imaging: a review , 2014, Journal of biomedical optics.

[17]  Christian R. Mora,et al.  Determination of Basic Density and Moisture Content of Loblolly Pine Wood Disks Using a near Infrared Hyperspectral Imaging System , 2011 .

[18]  Heinrich Spiecker,et al.  Detection and Classification of Norway Spruce Compression Wood in Reflected Light by Means of Hyperspectral Image Analysis , 2009 .

[19]  Mark O. Kimberley,et al.  Modelling the variation in wood density of New Zealand-grown Douglas-fir , 2017, New Zealand Journal of Forestry Science.

[20]  Richard F. Daniels,et al.  Near infrared spectroscopy for the nondestructive estimation of clear wood properties of Pinus taeda L . from the southern United States , 2005 .

[21]  Barbara L. Gartner,et al.  Assessing wood characteristics and wood quality in intensively managed plantations , 2005 .

[22]  R. Meder,et al.  Near Infrared Spectroscopic and Hyperspectral Imaging of Compression Wood in Pinus Radiata D. Don , 2012 .

[23]  Roger Meder,et al.  Near Infrared Hyperspectral Imaging Applied to Mapping Chemical Composition in Wood Samples , 2010 .

[24]  W. T. Adams,et al.  Intensively managed forest plantations in the pacific northwest: Conclusions , 2005 .

[25]  C. N. Filipescu,et al.  Variation of lumber properties in genetically improved full-sib families of Douglas-fir in British Columbia, Canada , 2018 .

[26]  S. Kelley,et al.  Use of near infrared spectroscopy to predict the mechanical properties of six softwoods , 2004 .

[27]  Brad Jianhe Wang,et al.  Acoustic Testing and Sorting of Chinese Poplar Logs for Structural LVL Products , 2013 .

[28]  Alfred Teischinger,et al.  The Relationship between near Infrared Spectra of Radial Wood Surfaces and Wood Mechanical Properties , 2001 .

[29]  Da-Wen Sun,et al.  Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications , 2017 .

[30]  O. Hagman,et al.  Multivariate prediction of wood surface features using an imaging spectrograph , 1997, Holz als Roh- und Werkstoff.

[31]  Satoru Tsuchikawa,et al.  Application of near Infrared Spectroscopy for Estimating Wood Mechanical Properties of Small Clear and Full Length Lumber Specimens , 2008 .

[32]  M. Manley.  Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials. , 2014, Chemical Society reviews.

[33]  Jorge Luis Monteiro de Matos,et al.  Comparison of Methods for Estimating Mechanical Properties of Wood by NIR Spectroscopy , 2018 .

[34]  A. Clark,et al.  High resolution scanning of radial strips cut from increment cores by near infrared spectroscopy , 2007 .

[35]  Resin defect detection in appearance lumber using 2D NIR spectroscopy , 2017, European Journal of Wood and Wood Products.

[36]  Brigitte Leblon,et al.  Using near-infrared hyperspectral images on subalpine fir board. Part 2: Density and basic specific gravity estimation , 2015 .

[37]  J. Harrington,et al.  Mapping Within-Stem Variation of Chemical Composition by near Infrared Hyperspectral Imaging , 2016 .

[38]  John R. Moore,et al.  Corewood (Juvenile Wood) and Its Impact on Wood Utilisation , 2017, Current Forestry Reports.

[39]  Richard F. Daniels,et al.  Variation in loblolly pine ring microfibril angle in the southeastern United States , 2007 .

[40]  Paul Scheunders,et al.  Close range hyperspectral imaging of plants: A review , 2017 .

[41]  Ronald D. Snee,et al.  Validation of Regression Models: Methods and Examples , 1977 .

[42]  A. Clark,et al.  Regional variation in wood specific gravity of planted loblolly pine in the United States , 2008 .

[43]  P. Laguna,et al.  Signal Processing , 2002, Yearbook of Medical Informatics.

[44]  Gilles Chaix,et al.  Resonance and near Infrared Spectroscopy for Evaluating Dynamic Wood Properties , 2010 .

[45]  Peter Carter,et al.  Sorting Logs and Lumber for Stiffness Using Director HM200 , 2007 .

[46]  D. Marshall,et al.  Plantation productivity in the Douglas-fir region under intensive silvicultural practices: results from research and operations. , 2005 .

[47]  Fabrice Meriaudeau,et al.  Automatic computation of the knot area ratio for machine strength grading of Douglas-fir and Spruce timber , 2010 .

[48]  P. Melo-Pinto,et al.  Measurement of intra-ring wood density by means of imaging VIS/NIR spectroscopy (hyperspectral imaging) , 2012 .

[49]  Roger Meder,et al.  Sawmill Trial of At-Line Prediction of Recovered Lumber Stiffness by NIR Spectroscopy of Pinus Radiata Cants , 2003 .

[50]  P. Hoffmeyer,et al.  Evaluation of density and strength of Norway spruce wood by near infrared reflectance spectroscopy , 2007, Holz als Roh- und Werkstoff.

[51]  Robert Evans,et al.  Wood Stiffness by X‐Ray Diffractometry , 2008 .

[52]  Vikas Vikram,et al.  Stiffness of Douglas-fir lumber : effects of wood properties and genetics , 2011 .

[53]  John R. Moore,et al.  Within- and between-stand variation in selected properties of Sitka spruce sawn timber in the UK: implications for segregation and grade recovery , 2013, Annals of Forest Science.

[54]  J. Dahlen,et al.  Probability models that relate nondestructive test methods to lumber design values of plantation loblolly pine , 2018 .

[55]  Andrew N. Gray,et al.  Timber resource statistics for nonnational forest land in western Washington, 2001. , 2005 .

[56]  P. D. Jones,et al.  Near-infrared spectroscopy prediction of southern pine No. 2 lumber physical and mechanical properties , 2016, Wood Science and Technology.

[57]  Robert J. Ross,et al.  Relationship between log and lumber modulus of elasticity , 1997 .

[58]  Richard F. Daniels,et al.  Acoustic evaluation of loblolly pine tree- and lumber-length logs allows for segregation of lumber modulus of elasticity, not for modulus of rupture , 2017, Annals of Forest Science.

[59]  B. Leblon,et al.  Using near-infrared hyperspectral images on subalpine fir board. Part 1: Moisture content estimation , 2015 .

[60]  Christian R. Mora,et al.  On the selection of samples for multivariate regression analysis : application to near-infrared (NIR) calibration models for the prediction of pulp yield in Eucalyptus nitens , 2008 .

[61]  Paul Geladi,et al.  Near Infrared Image Analysis for Online Identification and Separation of Wood Chips with Elevated Levels of Extractives , 2012 .

[62]  Satoru Tsuchikawa,et al.  Fast online NIR technique to predict MOE and moisture content of sawn lumber , 2014 .

[63]  Aoife A. Gowen,et al.  Data handling in hyperspectral image analysis , 2011 .

[64]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .