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Article

Non-Destructive Methodology to Determine Modulus of Elasticity in Static Bending of Quercus mongolica Using Near-Infrared Spectroscopy

by 1,2,3, 1,2,3, 1,2,3 and 1,2,3,*
1
School of Technology, Beijing Forestry University, Beijing 100083, China
2
Beijing Laboratory of Urban and Rural Ecological Environment, Beijing 100083, China
3
Key Lab of State Forestry Administration for Forestry Equipment and Automation, Beijing 10083, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(6), 1963; https://doi.org/10.3390/s18061963
Received: 14 May 2018 / Revised: 10 June 2018 / Accepted: 15 June 2018 / Published: 18 June 2018
(This article belongs to the Special Issue Spectroscopy Based Sensors)
This article presents a non-destructive methodology to determine the modulus of elasticity (MOE) in static bending of wood through the use of near-infrared (NIR) spectroscopy. Wood specimens were obtained from Quercus mongolica growing in Northeast of China. The NIR spectra of specimens were acquired by using a one-chip NIR fiber optic spectrometer whose spectral range was 900~1900 nm. The raw spectra of specimens were pretreated by multiplication scatter correlation and Savitzky-Golay smoothing and differentiation filter. To reduce the dimensions of data and complexity of modeling, the synergy interval partial least squares and successive projections algorithm were applied to extract the characteristic wavelengths, which had closing relevance with the MOE of wood, and five characteristic wavelengths were selected from full 117 variables of a spectrum. Taking the characteristic wavelengths as input values, partial least square regression (PLSR) and the propagation neural network (BPNN) were implemented to establish calibration models. The predictive ability of the models was estimated by the coefficient of determination (rp) and the root mean square error of prediction (RMSEP) and in the prediction set. In comparison with the predicted results of the models, BPNN performed better results with the higher rp of 0.91 and lower RMSEP of 0.76. The results indicate that it is feasible to accurately determine the MOE of wood by using the NIR spectroscopy technique. View Full-Text
Keywords: near-infrared spectroscopy; the modulus of elasticity in static bending; synergy interval partial least squares; successive projections algorithm; characteristic wavelengths near-infrared spectroscopy; the modulus of elasticity in static bending; synergy interval partial least squares; successive projections algorithm; characteristic wavelengths
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MDPI and ACS Style

Liang, H.; Zhang, M.; Gao, C.; Zhao, Y. Non-Destructive Methodology to Determine Modulus of Elasticity in Static Bending of Quercus mongolica Using Near-Infrared Spectroscopy. Sensors 2018, 18, 1963. https://doi.org/10.3390/s18061963

AMA Style

Liang H, Zhang M, Gao C, Zhao Y. Non-Destructive Methodology to Determine Modulus of Elasticity in Static Bending of Quercus mongolica Using Near-Infrared Spectroscopy. Sensors. 2018; 18(6):1963. https://doi.org/10.3390/s18061963

Chicago/Turabian Style

Liang, Hao, Meng Zhang, Chao Gao, and Yandong Zhao. 2018. "Non-Destructive Methodology to Determine Modulus of Elasticity in Static Bending of Quercus mongolica Using Near-Infrared Spectroscopy" Sensors 18, no. 6: 1963. https://doi.org/10.3390/s18061963

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