光谱学与光谱分析 |
|
|
|
|
|
Applied Study on Clustering of Variables around Latent Components Method in Wavelength Region Selection with Near-Infrared Spectroscopy |
BAO Feng-wei1,PENG Qian-rong2*,LIU Jing-yan3,CAI Yuan-qing2,MAO Han-bing2, TANG Ke2,Lü Yan-wen2 |
1. College of Chemical Engineering, Guizhou University, Guiyang 550003, China2. Technology Center,China National Tobacco Guizhou Industrial Corporation, Guiyang 550003, China3. College of Bioscience and Bioengineering, Hebei University of Science and Technology, Shijiazhuang 050018, China |
|
|
Abstract The present paper introduced the principle of clustering of variables around latent components method ,and used this method in selecting spectrum range of the NIR quantitative analysis models. Taking tobacco samples as experiment materials, we dealed with 107 sample spectra, divided the spectra into 5 clusters, and explained the information reflected by each of these 5 clusters in terms of chemistry. On this basis, we chose the corresponding wavelength range to set up the quantitative models of the total sugar, reducing sugar and nicotine by PLS method. Compared with the model based on the full NIR spectral range, Rtraining of the models based on the chosen spectral range rose from 0.977 1,0.917 2 and 0.987 4 to 0.995 5,0.975 1 and 0.994 4;Rtest rose from 0.977 8,0.941 2 and 0.993 2 to 0.992 7,0.967 9 and 0.994 0;RMSECV dropped from 1.09,1.43,0.14 to 1.05,1.05 and 0.13, RMSEP dropped from 0.92,1.17 and 0.16 to 0.39,0.63 and 0.11 and the D value dropped from 1.274%,1.972% and 0.829% to 0.711%,0.843% and 0.768% for the total sugar,reducing sugar and nicotine, respectively. These data indicated that this method can improve the forecasting precision and stability of the model, so offers certain guidance on practical application.
|
Received: 2006-05-10
Accepted: 2006-08-20
|
|
Corresponding Authors:
PENG Qian-rong
E-mail: pengqr@public.gz.cn
|
|
[1] LU Wan-zhen,YUAN Hong-fu,XU Guang-tong(陆婉珍,袁洪福,徐广通, 等). Modern Near-Infrared Spectroscopic Analysis Technique(现代近红外光谱分析技术). Beijing:China Petrochemical Press(北京:中国石化出版社),2000. 6. [2] YAN Yan-lu,ZHAO Long-lian,LI Jun-hui, et al(严衍禄,赵龙莲,李军会, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2000,20(6):777. [3] WANG Fang,CHEN Da,SHAO Xue-guang(王 芳,陈 达,邵学广). Tobacco Science & Technology(烟草科技),2002,(5):23. [4] MA Xiang,WANG Yi,WEN Ya-dong, et al(马 翔,王 毅,温亚东, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2004,24(4):444. [5] ZHANG Lu-da,ZHAO Li-li,ZHAO Long-lian, et al(张录达,赵丽丽,赵龙莲, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2005,25(8):1227. [6] CHU Xiao-li,YUAN Hong-fu,LU Wan-zhen(褚小立,袁洪福,陆婉珍). Progress in Chemistry(化学进展),2004,16(4):535. [7] Forina M,Casolino C,Pizarro Millan C. Journal of Chemometrics,1999,13(2):165. [8] Brandye M Smith,Paul J Gemperline. Analytica Chimica Acta,2000,423(2):167. [9] Vigneau E,Qannari E M. Communications in Statistics-Simulation and Computation,2003,32(4):1131. [10] Vigneau E,Sahmer K,Qannariand E M, et al. Journal of Chemometrics,2005,19(3):122. [11] Heronides Adonias Dantas Filho,Roberto Kawakami Harrop Galvao,Mário Cesar Ugulino Araújo, et al. Chemometrics and Intelligent Laboratory Systems,2004,72(1):83. [12] XU Guang-tong,YUAN Hong-fu,LU Wan-zhen(徐广通,袁洪福,陆婉珍). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2000,20(2):134. [13] Donald A Burns,Emil W Ciurczak. Handbook of Near-Infrared Analysis,New York:Marcel Dekker,Inc.,1992.
|
[1] |
WANG Wen-xiu, PENG Yan-kun*, FANG Xiao-qian, BU Xiao-pu. Characteristic Variables Optimization for TVB-N in Pork Based on Two-Dimensional Correlation Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2094-2100. |
[2] |
LE Ba Tuan1, 3, XIAO Dong1*, MAO Ya-chun2, SONG Liang2, HE Da-kuo1, LIU Shan-jun2. Coal Classification Based on Visible, Near-Infrared Spectroscopy and CNN-ELM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2107-2112. |
[3] |
LIU Jin, LUAN Xiao-li*, LIU Fei. Near Infrared Spectroscopic Modelling of Sodium Content in Oil Sands Based on Lasso Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2274-2278. |
[4] |
YU Hui-ling1, MEN Hong-sheng2, LIANG Hao2, ZHANG Yi-zhuo2*. Near Infrared Spectroscopy Identification Method of Wood Surface Defects Based on SA-PBT-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1724-1728. |
[5] |
XU Wei-jie1, WU Zhong-chen1, 2*, ZHU Xiang-ping2, ZHANG Jiang1, LING Zong-cheng1, NI Yu-heng1, GUO Kai-chen1. Classification and Discrimination of Martian-Related Minerals Using Spectral Fusion Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1926-1932. |
[6] |
LI Ying1, LI Yao-xiang1*, LI Wen-bin2, JIANG Li-chun3. Model Optimization of Wood Property and Quality Tracing Based on Wavelet Transform and NIR Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1384-1392. |
[7] |
DU Jian1, 2, HU Bing-liang1*, LIU Yong-zheng1, WEI Cui-yu1, ZHANG Geng1, TANG Xing-jia1. Study on Quality Identification of Macadamia nut Based on Convolutional Neural Networks and Spectral Features[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1514-1519. |
[8] |
HAN Guang, LIU Rong*, XU Ke-xin. Extraction of Effective Signal in Non-Invasive Blood Glucose Sensing with Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1599-1604. |
[9] |
WANG Li-shuang, ZHANG Wen-bo*, TONG Li. Studies on Dimensional Stability of Wood under Different Moisture Conditions by Near Infrared Spectroscopy Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1066-1069. |
[10] |
HUANG Hua1, WU Xi-yu2, ZHU Shi-ping1*. Feature Wavelength Selection and Efficiency Analysis for Paddy Moisture Content Prediction by Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1070-1075. |
[11] |
LI Hao-guang1,2, YU Yun-hua1,2, PANG Yan1, SHEN Xue-feng1,2. Study of Maize Haploid Identification Based on Oil Content Detection with Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1089-1094. |
[12] |
PENG Cheng1, FENG Xu-ping2*, HE Yong2, ZHANG Chu2, ZHAO Yi-ying2, XU Jun-feng1. Discrimination of Transgenic Maize Containing the Cry1Ab/Cry2Aj and G10evo Genes Using Near Infrared Spectroscopy (NIR)[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1095-1100. |
[13] |
XIA Ji-an1, YANG Yu-wang1*, CAO Hong-xin2, HAN Chen1, GE Dao-kuo2, ZHANG Wen-yu2. Classification of Broad Bean Pest of Visible-Near Infrared Spectroscopy Based on Cloud Computing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 756-760. |
[14] |
MAO Ya-chun, WANG Dong, WANG Yue, LIU Shan-jun*. A FeO/TFe Determination Method of BIF Based on the Visible and Near-Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 765-770. |
[15] |
LAI Tian-yue1, CAI Feng-huang1*, PENG Xin2*, CHAI Qin-qin1, LI Yu-rong1, 3, WANG Wu1, 3. Identification of Tetrastigma hemsleyanum from Different Places with FT-NIR Combined with Kernel Density Estimation Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 794-799. |
|
|
|
|