[1] 宋小燕, 白福忠, 武建新, 等.应用灰度直方图特征识别木材表面节子缺陷[J].激光与光电子学进展, 2015, 52(3):199-204. http://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201503027.htm

SONG Xiaoyan, BAI Fuzhong, WU Jianxin, et al. Wood knot defects recognition with gray-scale histogram features[J]. Laser Optoelectron Prog, 2015, 52(3):199-204. http://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201503027.htm
[2] KARSULOVIC J T, LEÓN L A, GAETE L. Ultrasonic detection of knots and annual ring orientation in Pinus radiata lumber[J]. Wood Fiber Sci, 2007, 32(3):278-286. https://www.cabdirect.org/cabdirect/abstract/20000613150
[3] MACHADO J S, SARDINHA R A, CRUZ H P. Feasibility of automatic detection of knots in maritime pine timber by acousto-ultrasonic scanning[J]. Wood Sci Technol, 2004, 38(4):277-284. doi:  10.1007%2Fs00226-004-0224-x.pdf
[4] BARADIT E, AEDO R, CORREA J. Knots detection in wood using microwaves[J]. Wood Sci Technol, 2006, 40(2):118-123. doi:  10.1007/s00226-005-0027-8
[5] CRISTHIAN A C, SANCHEZ R, BARADIT E. Detection of knots using x-ray tomographies and deformable contours with simulated annealing[J]. Wood Res, 2008, 53(2):57-66. https://www.researchgate.net/publication/288687379_Detection_of_knots_using_X-ray_tomographies_and_deformable_contours_with_simulated_annealing
[6] LONGUETAUD F, MOTHE F, KERAUTRET B, et al. Automatic knot detection and measurements from X-ray CT images of wood:a review and validation of an improved algorithm on softwood samples[J]. Comput Electron Agric, 2012, 85(5):77-89. https://hal.archives-ouvertes.fr/docs/00/78/07/61/PDF/2012Longuetaud.pdf
[7] CHEN Lijun, WANG Keqi, XIE Yonghua, et al. The segmentation of timber defects based on color and the mathematical morphology[J]. Optik-Int J Light Elect Opt, 2014, 125(3):965-967. doi:  10.1016/j.ijleo.2013.07.098
[8] XIE Yonghua, WANG Jingcong. Study on the identification of the wood surface defects based on texture features[J]. Optik-Int J Light Electr Opt, 2015, 126(19):2231-2235. doi:  10.1016/j.ijleo.2015.05.101
[9] POKE F S, RAYMOND C A. Predicting extractives, lignin, and cellulose contents using near infrared spectroscopy on solid wood in Eucalyptus globules[J]. J Wood Chem Technol, 2006, 26(2):187-199. doi:  10.1080/02773810600732708
[10] ANDRÉN, LABBÉN, RIALS T G, et al. Assessment of wood load condition by near infrared (NIR) spectroscopy[J]. J Mater Sci, 2006, 41(7):1879-1886. doi:  10.1007/s10853-006-4433-6
[11] COGDILL R P, SCHIMLECK L R, JONES P D, et al. Estimation of the physical wood properties of Pinus taeda L. radial strips using least squares support vector machines[J]. J Near Infrared Spectrosc, 2004, 12(4):263-270.
[12] 杨忠, 黄安民, 江泽慧.近红外光谱结合回归分析预测法判别木材的生物腐朽[J].林业科学, 2012, 48(10):120-124. doi:  10.11707/j.1001-7488.20121018

YANG Zhong, HUANG Anmin, JIANG Zehui. Discrimination of wood biological decay by NIR coupled with regression analysis prediction method[J]. Sci Silv Sin, 2012, 48(10):120-124. doi:  10.11707/j.1001-7488.20121018
[13] FUJIMOTO T, TSUCHIKAWA S. Identification of dead and sound knots by near infrared spectroscopy[J]. J Near Infrared Spectrosc, 2010, 18(6):473-479.
[14] YANG Zhong, ZHANG Maomao, CHEN Ling, et al. Non-contact detection of surface quality of knot defects on eucalypt veneers by near infrared spectroscopy coupled with soft independent modeling of class analogy[J]. BioResources, 2015, 10(2):3314-3325.
[15] 杨忠, 陈玲, 付跃进, 等.近红外光谱结合SIMCA模式识别法检测木材表面节子[J].东北林业大学学报, 2012, 40(8):70-72. http://www.cnki.com.cn/Article/CJFDTOTAL-DBLY201208016.htm

YANG Zhong, CHEN Ling, FU Yuejin, et al. Rapid detection of knot defect in wood surface by near infrared spectroscopy coupled with SIMCA pattern recognition[J]. J Northeast For Univ, 2012, 40(8):70-72. http://www.cnki.com.cn/Article/CJFDTOTAL-DBLY201208016.htm
[16] KENNARD R W, STONE L A. Computer aided design of experiments[J]. Technometrics, 1969, 11(1):137-148. doi:  10.1080/00401706.1969.10490666
[17] 褚小立, 袁洪福, 陆婉珍.近红外分析中光谱预处理及波长选择方法进展与应用[J].化学进展, 2004, 16(4):528-542. http://www.cnki.com.cn/Article/CJFDTOTAL-YWFX201005062.htm

CHU Xiaoli, YUAN Hongfu, LU Wanzhen. Progress and application of spectral data pretreatment and wavelength selection methods in NIR analytical technique[J]. Prog Chem, 2004, 16(4):528-542. http://www.cnki.com.cn/Article/CJFDTOTAL-YWFX201005062.htm
[18] 郑剑, 周竹, 仲山民, 等.基于近红外光谱与随机青蛙算法的褐变板栗识别[J].浙江农林大学学报, 2016, 33(2):322-329. doi:  10.11833/j.issn.2095-0756.2016.02.019

ZHENG Jian, ZHOU Zhu, ZHONG Shanmin, et al. Chestnut browning detected with near-infrared spectroscopy and a random-frog algorithm[J]. J Zhejiang A & F Univ, 2016, 33(2):322-329. doi:  10.11833/j.issn.2095-0756.2016.02.019
[19] 郑剑, 周竹, 仲山民, 等.基于近红外光谱的褐变板栗识别建模方法研究[J].食品科技, 2016, 41(1):285-290. http://www.cnki.com.cn/Article/CJFDTOTAL-SSPJ201601062.htm

ZHENG Jian, ZHOU Zhu, ZHONG Shanmin, et al. Detection of chestnut browning using different calibration methods based on near-infrared spectroscopy[J]. Food Sci Technol, 2016, 41(1):285-290. http://www.cnki.com.cn/Article/CJFDTOTAL-SSPJ201601062.htm
[20] SUYKENS J A K, de BRABANTER J, LUKAS L, et al. Weighted least squares support vector machines:robustness and sparse approximation[J]. Neurocomputing, 2002, 48(1):85-105.
[21] LI Hongdong, XU Qingsong, LIANG Yizeng, et al. Random frog:an efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification[J]. Anal Chim Acta, 2012, 740:20-26. doi:  10.1016/j.aca.2012.06.031
[22] 李洪东. 广义灰色分析体系建模的基本问题及其模型集群分析研究[D]. 长沙: 中南大学, 2012.

LI Hongdong. Fundamental Problems of Generalized Grey Analytical Systems and Their Research of Model Population Analysis[D]. Changsha:Central South University, 2012.
[23] ALVES A, SANTOS A, ROZENBERG P, et al. A common near infrared-based partial least squares regression model for the prediction of wood density of Pinus pinaster and Larix×eurolepis[J]. Wood Sci Technol, 2012, 46(1):157-175.
[24] SCHWANNINGER M, RODRIGUES J C, FACKLER K. A review of band assignments in near infrared spectra of wood and wood components[J]. J Near Infrared Spectrosc, 2011, 19(5):287-308. doi:  10.1255/jnirs.955