光谱学与光谱分析 |
|
|
|
|
|
Prediction of Cellulose, Hemicellulose, Lignin and Ash Content of Four Miscanthus Bio-Energy Crops Using Near-Infrared Spectroscopy |
LI Xiao-na, FAN Xi-feng*, WU Ju-ying, ZHANG Guo-fang, LIU Shang-yi, WU Mei-jun, CHENG Yan-bo, ZHANG Nan |
Beijing Research & Development Center for Grass and Environment, Beijing 100097, China |
|
|
Abstract Biomass energy is being industrialized rapidly in China in recent years, whereas, research on energy grass is still in primary stage. Only if near-infrared spectroscopy mode was constructed which was used to predict the lignin, cellulose and hemicellulose contents in energy crop, the varieties screening, performance evaluation and on-line control of industrialization would be facilitated. In this study, the prediction model for quality indices (cellulose, hemicellulose, lignin and ash) of four energy grass (Miscanthus) was built using Fourier transform near-infrared (FT-NIR) spectroscopy combined with partial least squares regression (PLSR) , and the impacts exerted by particle size on the model were also revealed. The results showed that (1) the root mean error of cross validation (RMSECV) of cellulose, hemicelluloses and lignin contents were 1.35% (R2=0.88), 0.39% (R2=0.91) and 0.35 (R2=0.80), respectively in stalk and 0.72% (R2=0.88), 0.85% (R2=0.85) and 0.44 (R2=0.87), respectively in leaf. The model showed good performance in prediction of corresponding contents in unknown samples, however, no satisfying performance in ash content. (2) Both 2 mm and 0.5 mm grades of particle size can meet accuracy requirements of the model. But considering the time and labor cost, 2 mm grade was suggested for model building.
|
Received: 2014-10-14
Accepted: 2015-02-05
|
|
Corresponding Authors:
FAN Xi-feng
E-mail: fanxifengcau@163.com
|
|
[1] Anon. Department of the Taoiseach, Dublin, 2008. [2] Orts W J, Holtmant, K M, Seiber J N. Journal of Agricultural and Food Chemistry, 2008, 56: 3892. [3] Hames B R, Thomas S R, Sluiter A D. Applied Biochemistry and Biotechnology, 2003,105: 5. [4] Lu Liu, X Philip Ye, Alvin R. Carbohydrate Polymers, 2010, 81: 820. [5] X Philip Ye, Lu Liu, Douglas Hayes. Bioresource Technology, 2008, 99: 7323. [6] Colette C Fagan, Colm D Everard, Kevin McDonnell. Bioresource Technology, 2011,102: 5200. [7] CHENG Xu-yun, NIU Zhi-you, YAN Hong-mei(程旭云,牛智有,晏红梅). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2013, 29(11):196. [8] YANG Zeng-ling, XUE Jun-jie, HE Cheng(杨增玲,薛俊杰,贺 城). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报),2013, 44(8):139. [9] General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Standardization Administration of China(中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会). Determination of Acid Detergent Lignin in Feedstuffs(酸性洗涤木质素测定), 2006,(GB/T 20805—2006). [10] General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Standardization Administration of China(中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会). Determination of Neutral Detergent Fiber in Feedstuffs(中性洗涤纤维测定),2006,(GB/T 20806—2006). [11] Burns D A,Ciurczak E W. Handbook of Near-Infrared Analysis, 2nd ed CRC. New York: Marcel Dekker,2001. 431. [12] Williams P. Near-Infrared Technology-Getting the Best Cut of Light. A Short Course in the Practical Inplementation of Near Infrared Spectroscopy. 1.1 ed. Nanaimo, Canada,2003. [13] Haffner F B, Mitchell V D, Arundale R A, et al. Cellulose, 2013, 20(4): 1629. [14] Jiang W, Han G T, Via B K. Wood Science and Technology, 2014, 48(1): 109. |
[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] |
WANG Xin-qiang1, 3, CHU Pei-zhu1, 3, XIONG Wei2, 4, YE Song1, 3, GAN Yong-ying1, 3, ZHANG Wen-tao1, 3, LI Shu1, 3, WANG Fang-yuan1, 3*. Study on Monomer Simulation of Cellulose Raman Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 164-168. |
[3] |
LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei*, PAN Shao-kui. Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 176-180. |
[4] |
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. |
[5] |
HE Qing-yuan1, 2, REN Yi1, 2, LIU Jing-hua1, 2, LIU Li1, 2, YANG Hao1, 2, LI Zheng-peng1, 2, ZHAN Qiu-wen1, 2*. Study on Rapid Determination of Qualities of Alfalfa Hay Based on NIRS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3753-3757. |
[6] |
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. |
[7] |
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. |
[8] |
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. |
[9] |
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. |
[10] |
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. |
[11] |
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. |
[12] |
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. |
[13] |
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. |
[14] |
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. |
[15] |
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. |
|
|
|
|