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ARS Home » Southeast Area » Athens, Georgia » U.S. National Poultry Research Center » Quality & Safety Assessment Research » Research » Publications at this Location » Publication #363842

Research Project: Assessment and Improvement of Poultry Meat, Egg, and Feed Quality

Location: Quality & Safety Assessment Research

Title: Prediction of Douglas-Fir lumber properties: comparison between a benchtop near-infrared spectrometer and hyperspectral imaging system

Author
item SCHIMLECK, LAURENCE - Oregon State University
item DAHLEN, JOSEPH - University Of Georgia
item Yoon, Seung-Chul
item Lawrence, Kurt
item JONES, PAUL - Benchmark International

Submitted to: Applied Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/10/2018
Publication Date: 12/13/2018
Citation: Schimleck, L., Dahlen, J., Yoon, S.C., Lawrence, K.C., Jones, P.D. 2018. Prediction of Douglas-Fir lumber properties: comparison between a benchtop near-infrared spectrometer and hyperspectral imaging system. Applied Sciences. 8(12):2602.

Interpretive Summary: Due to the variable nature of wood, there is a need to better segregate high-quality material from low-quality material before sawing. Near-infrared (NIR) spectroscopy and NIR hyperspectral imaging (NIR-HSI) were compared as rapid and non-destructive sensing techniques for the estimation of physical and mechanical properties of Douglas-fir structural lumber. The study found that while the results were similar between the two systems, the use of a NIR-HSI system had several significant advantages over a typical NIR benchtop instrument, because of the ability to predict the spatial properties of a sample and the greater versatility. In time, NIR-HSI may provide lumber mills with a cost-effective method to efficiently identify areas of high and low stiffness within logs prior to sawing, and thus would allow mills to make more informed decisions on a log-by-log basis for sawing solutions.

Technical Abstract: 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.