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基于降噪处理的蒙古栎木材气干密度NIRS定标模型(PDF)

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

Issue:
2016年06期
Page:
148-156
Column:
研究论文
publishdate:
2016-11-30

Article Info:/Info

Title:
Model calibrating for NIRS-based oak wood air-dry density prediction with denoising pretreatment
Article ID:
1000-2006(2016)06-0148-09
Author(s):
LI Ying1 LI Yaoxiang1* XU Haokai1 JIANG Lichun2
1.College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China;
2. College of Forestry, Northeast Forestry University, Harbin 150040, China
Keywords:
Quercus mongolica Fisch. ex Ledeb wood air-dry density near infrared spectrum(NIRS) denoising
Classification number :
S781.31
DOI:
10.3969/j.issn.1000-2006.2016.06.023
Document Code:
A
Abstract:
Savitzky-Golay smoothing and wavelet transform were applied to the near infrared spectrum(NIRS)of oak(Quercus mongolica Fisch. ex Ledeb)wood for the noise elimination. Denoising effects were discussed by switching the pretreatment order of the two methods. Partial least squares regression(PLS)and principal component regression(PCR)were used for model development of oak wood air-dry density prediction. The results showed that the best denoising effects were associated with spectrum pretreatment of SG smoothing followed by wavelet transform with three smoothing points and two db5 wavelet decomposition layers. The signal to noise ratio(SNR)was 18.546 and the root mean square error of calibration was 0.04. For the calibration model of NIR-based oak wood air-dry density prediction developed with PLS, the determination coefficient(R2)was 0.902 for the calibration model with correct root mean square error reduced by 35.32% compared to the model developed with raw spectrum. R2 for prediction was 0.860 with residual prediction deviation of 2.67. This study indicated that NIR could be used for rapid determination of oak wood air-dry density, and determination of reasonable processing parameters as well as modeling methods could effectively improve the model accuracy.

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Last Update: 2016-11-20