Modeling Wood Crystallinity with Multiple Linear Regression

被引：0
|
作者：
Li, Yaoxiang [2 ]
Jiang, Lichun [1 ]
机构：
[1] Northeast Forestry Univ, Coll Forestry, Harbin, Heilongjiang, Peoples R China
[2] Northeast Forestry Univ, Coll Engn & Technol, Harbin, Heilongjiang, Peoples R China
来源：
MATERIALS ENGINEERING FOR ADVANCED TECHNOLOGIES, PTS 1 AND 2 | 2011年 / 480-481卷
关键词：
multiple linear regression; wood crystallinity; near infrared spectroscopy; NEAR-INFRARED SPECTROSCOPY;
D O I：
10.4028/www.scientific.net/KEM.480-481.550
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The crystallinity of wood has an important effect on the physical, mechanical and chemical properties of cellulose fibers. Crystallinity of larch plantation wood was investigated with near infrared spectroscopy and multiple linear regression. Five typical wave lengths were selected to establish prediction model for wood crystallinity. Full-cross validation was applied to the model development. The model performance is satisfied with prediction correlation coefficient of 0.896 and bias of 0.0004. The results indicated that prediction of wood crystallinity with near infrared spectroscopy and multiple linear regression is feasible, which provides a fast and nondestructive method for wood crystallinity prediction.
引用
收藏
页码：550 / +
页数：2
相关论文
共 50 条
  • [1] Orographic precipitation modeling with multiple linear regression
    Naoum, S
    Tsanis, IK
    [J]. JOURNAL OF HYDROLOGIC ENGINEERING, 2004, 9 (02) : 79 - 102
  • [2] Multiple linear regression modeling for compositional data
    Wang, Huiwen
    Shangguan, Liying
    Wu, Junjie
    Guan, Rong
    [J]. NEUROCOMPUTING, 2013, 122 : 490 - 500
  • [3] Modeling Pan Evaporation for Kuwait by Multiple Linear Regression
    Almedeij, Jaber
    [J]. SCIENTIFIC WORLD JOURNAL, 2012,
  • [4] Analysis and modeling of pathogenicity loci based on multiple linear regression
    Zhou, Junjie
    Yang, Liu
    Lu, Hua
    [J]. BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2019, 125 : 217 - 217
  • [5] Probabilistic Multiple Linear Regression Modeling for Tropical Cyclone Intensity
    Lee, Chia-Ying
    Tippett, Michael K.
    Camargo, Suzana J.
    Sobel, Adam H.
    [J]. MONTHLY WEATHER REVIEW, 2015, 143 (03) : 933 - 954
  • [6] MODELING THE TOXICITY OF ORGANOPHOSPHATES - A COMPARISON OF THE MULTIPLE LINEAR-REGRESSION AND PLS REGRESSION METHODS
    VERHAAR, HJM
    ERIKSSON, L
    SJOSTROM, M
    SCHUURMANN, G
    SEINEN, W
    HERMENS, JLM
    [J]. QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS, 1994, 13 (02): : 133 - 143
  • [7] MODELING AND COMPARISON OF BONDING STRENGTH OF IMPREGNATED WOOD MATERIAL BY USING DIFFERENT METHODS: ARTIFICIAL NEURAL NETWORK AND MULTIPLE LINEAR REGRESSION
    Akyuz, Ilker
    Ersen, Nadir
    Tiryaki, Sebahattin
    Bayram, Bahadir Cagri
    Akyuz, Kadri Cemil
    Peker, Huseyin
    [J]. WOOD RESEARCH, 2019, 64 (03) : 483 - 497
  • [8] Multiple linear regression
    Martin Krzywinski
    Naomi Altman
    [J]. Nature Methods, 2015, 12 : 1103 - 1104
  • [9] Regression: multiple linear
    Bangdiwala, Shrikant I.
    [J]. INTERNATIONAL JOURNAL OF INJURY CONTROL AND SAFETY PROMOTION, 2018, 25 (02) : 232 - 236
  • [10] Multiple linear regression
    Krzywinski, Martin
    Altman, Naomi
    [J]. NATURE METHODS, 2015, 12 (12) : 1103 - 1104
12345