光谱学与光谱分析 |
|
|
|
|
|
Determination of Crystallinity in Neosinocalamus affinins Based on Near Infrared Spectroscopy and PLS Methods |
SUN Bai-ling, LIU Jun-liang*, CAI Yu-bo |
Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing 100091,China |
|
|
Abstract Near infrared spectroscopy technique combined with chemometrics methods was applied to predict crystallinity of Neosinocalamus affinins. Three improved partial least squares (PLS) methods, including interval partial least squares (iPLS), synergy interval partial least squares (siPLS) and backward interval partial least squares (biPLS), were used to find the most informative ranges and build models with better predictive quality based on multiplicative scatter correction spectra. And then the models were compared with PLS model which was developed on the whole wavelength range 350~2 500 nm. The results showed that the models built by the three improved PLS methods had higher predictive ability than that of PLS model, and the optimal model was obtained by siPLS method that separated the whole spectra into 30 intervals and combined three intervals. The siPLS model had correlation coefficient (R) of 0.88 and root mean standard error of prediction (RMSEP) of 0.011 7. Therefore, through selecting the effective wavelength range, siPLS method could accurately and rapidly predict crystallinity in Neosinocalamus affinins.
|
Received: 2010-04-23
Accepted: 2010-08-08
|
|
Corresponding Authors:
LIU Jun-liang
E-mail: liujunliang@caf.ac.cn
|
|
[1] LIU Yun-fei, XUE Lian-feng, RUAN Xi-gen, et al(刘云飞, 薛联凤, 阮锡根, 等). Journal of Nanjing Forestry University(Natural Science Edition)(南京林业大学学报·自然科学版), 2006, 30(6): 66. [2] TANG Jin-gen, ZHANG Chun-xia(唐进根, 张春霞). Bamboo Research(竹类研究), 1991, (1): 22. [3] Via B K, Shupe T F, Groom L H, et al. Journal of Near Infrared Spectroscopy, 2003, 11(5): 365. [4] Taylor A M, Baek S H, Jeong M K, et al. Wood and Fiber Science, 2008, 40(2): 301. [5] YU Hua-qiang, ZHAO Rong-jun, FU Feng, et al(虞华强, 赵荣军, 傅 峰, 等). Journal of Northwest Forestry University(西北林学院学报), 2007, 22(5): 149. [6] Jones P D, Schimleck L R, Peter G F, et al. Wood Science and Technology, 2006, 40(8): 709. [7] DING Li, XIANG Yu-hong, HUANG An-min, et al(丁 丽,相玉红,黄安民,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2009, 29(7): 1784. [8] Via B K, Shupe T F, Stine M, et al. Holz Als Roh-Und Werkstoff, 2005, 63(3): 231. [9] Jones P D, Schimleck L R, Peter G F, et al. Wood Science and Technology, 2005, 39(7): 85. [10] WANG Yu-rong, QIN Dao-chun, REN Hai-qing, et al(王玉荣, 覃道春, 任海青, 等). Wood Processing Machinery(木材加工机械), 2007, 18(3): 34. [11] Jiang Zehui, Yang Zhong, So Chileung, et al. Journal of Wood Science, 2007, 53(5): 449. [12] Nφrgaard L, Saudland A J, Wagner J, et al. Applied Spectroscopy, 2000, 54: 413. [13] ZHOU Fang-chun(周芳纯). Bamboo Research(竹类研究), 1998, (1): 195.
|
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[7] |
DANG Rui, GAO Zi-ang, ZHANG Tong, WANG Jia-xing. Lighting Damage Model of Silk Cultural Relics in Museum Collections Based on Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3930-3936. |
[8] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[9] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[10] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[11] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[12] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[13] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[14] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
[15] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
|
|
|
|