Determination of Hemicellulose, Cellulose and Lignin in Moso Bamboo by Near Infrared Spectroscopy

被引:169
作者
Li, Xiaoli [1 ]
Sun, Chanjun [1 ]
Zhou, Binxiong [1 ]
He, Yong [1 ]
机构
[1] Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
SUPPORT VECTOR MACHINES; REFLECTANCE SPECTROSCOPY; WAVELET TRANSFORM; PREDICTION; QUALITY; DIFFERENTIATION; ALGORITHM;
D O I
10.1038/srep17210
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The contents of hemicellulose, cellulose and lignin are important for moso bamboo processing in biomass energy industry. The feasibility of using near infrared (NIR) spectroscopy for rapid determination of hemicellulose, cellulose and lignin was investigated in this study. Initially, the linear relationship between bamboo components and their NIR spectroscopy was established. Subsequently, successive projections algorithm (SPA) was used to detect characteristic wavelengths for establishing the convenient models. For hemicellulose, cellulose and lignin, 22, 22 and 20 characteristic wavelengths were obtained, respectively. Nonlinear determination models were subsequently built by an artificial neural network (ANN) and a least-squares support vector machine (LS-SVM) based on characteristic wavelengths. The LS-SVM models for predicting hemicellulose, cellulose and lignin all obtained excellent results with high determination coefficients of 0.921, 0.909 and 0.892 respectively. These results demonstrated that NIR spectroscopy combined with SPA-LS-SVM is a useful, nondestructive tool for the determinations of hemicellulose, cellulose and lignin in moso bamboo.
引用
收藏
页数:11
相关论文
共 44 条
[1]   The successive projections algorithm for variable selection in spectroscopic multicomponent analysis [J].
Araújo, MCU ;
Saldanha, TCB ;
Galvao, RKH ;
Yoneyama, T ;
Chame, HC ;
Visani, V .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2001, 57 (02) :65-73
[2]   Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time-frequency vibration data [J].
Bordoloi, D. J. ;
Tiwari, Rajiv .
MEASUREMENT, 2014, 55 :1-14
[3]   Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk [J].
Borin, Alessandra ;
Ferrao, Marco Flores ;
Mello, Cesar ;
Maretto, Danilo Althmann ;
Poppi, Ronei Jesus .
ANALYTICA CHIMICA ACTA, 2006, 579 (01) :25-32
[4]  
Broomhead D. S., 1988, Complex Systems, V2, P321
[5]   Waveband selection for NIR spectroscopy analysis of soil organic matter based on SG smoothing and MWPLS methods [J].
Chen, Huazhou ;
Pan, Tao ;
Chen, Jiemei ;
Lu, Qipeng .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2011, 107 (01) :139-146
[6]   Detecting the quality of glycerol monolaurate: A method for using Fourier transform infrared spectroscopy with wavelet transform and modified uninformative variable elimination [J].
Chen, Xiaojing ;
Wu, Di ;
He, Yong ;
Liu, Shou .
ANALYTICA CHIMICA ACTA, 2009, 638 (01) :16-22
[7]   A model predictive control approach with relevant identification in dynamic PLS framework [J].
Chi, Qinghua ;
Fei, Zhengshun ;
Zhao, Zhao ;
Zhao, Li ;
Liang, Jun .
CONTROL ENGINEERING PRACTICE, 2014, 22 :181-193
[8]   THE WAVELET TRANSFORM, TIME-FREQUENCY LOCALIZATION AND SIGNAL ANALYSIS [J].
DAUBECHIES, I .
IEEE TRANSACTIONS ON INFORMATION THEORY, 1990, 36 (05) :961-1005
[9]   Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils [J].
Devos, Olivier ;
Downey, Gerard ;
Duponchel, Ludovic .
FOOD CHEMISTRY, 2014, 148 :124-130
[10]   Prediction of pork quality attributes from near infrared reflectance spectra [J].
Geesink, GH ;
Schreutelkamp, FH ;
Frankhuizen, R ;
Vedder, HW ;
Faber, NM ;
Kranen, RW ;
Gerritzen, MA .
MEAT SCIENCE, 2003, 65 (01) :661-668