| 光谱学与光谱分析 |
|
|
|
|
|
| Spectroscopy Quantitative Analysis and Optimum Wavelength Selection |
| ZHANG Man1, LIU Xu-hua1, HE Xiong-kui1，ZHANG Lu-da1*, ZHAO Long-lian2, LI Jun-hui2 |
1. College of Science, China Agricultural University, Beijing 100083, China
2. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China |
|
|
|
|
Abstract In the present paper, taking 66 wheat samples for testing materials, ridge regression technology in near-infrared (NIR) spectroscopy quantitative analysis was researched. The NIR-ridge regression model for determination of protein content was established by NIR spectral data of 44 wheat samples to predict the protein content of the other 22 samples. The average relative error was 0.015 18 between the predictive results and Kjeldahl’s values (chemical analysis values). And the predictive results were compared with those values derived through partial least squares (PLS) method, showing that ridge regression method was deserved to be chosen for NIR spectroscopy quantitative analysis. Furthermore, in order to reduce the disturbance to predictive capacity of the quantitative analysis model resulting from irrelevant information, one effective way is to screen the wavelength information. In order to select the spectral information with more content information and stronger relativity with the composition or the nature of the samples to improve the model’s predictive accuracy, ridge regression was used to select wavelength information in this paper. The NIR-ridge regression model was established with the spectral information at 4 wavelength points, which were selected from 1 297 wavelength points, to predict the protein content of the 22 samples. The average relative error was 0.013 7 and the correlation coefficient reached 0.981 7 between the predictive results and Kjeldahl’s values. The results showed that ridge regression was able to screen the essential wavelength information from a large amount of spectral information. It not only can simplify the model and effectively reduce the disturbance resulting from collinearity information，but also has practical significance for designing special NIR analysis instrument for analyzing specific component in some special samples.
|
|
Received: 2009-06-06
Accepted: 2009-09-08
|
|
|
|
Corresponding Authors:
ZHANG Lu-da
E-mail: zhangld@cau.edu.cn
|
|
[1] SUN Tong, XU Hui-rong, YING Yi-bin(孙 通, 徐惠荣, 应义斌). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2009，29(1)：122.
[2] SU Qing-quan, ZHANG Jin-sheng, GUO Qi (苏清泉，张金生，郭 奇). Contemporary Chemical Industry (当代化工), 2007, 36(6): 652.
[3] ZHOU Shu-ping, CHENG Gui-min, LI Wei-hong, et al(周淑平, 程贵敏, 李卫红, 等). Guizhou Agricultural Sciences(贵州农业科学), 2007, 35(1): 28.
[4] ZHANG Hui, SONG Yan, LENG Jing, et al(张 卉, 宋 妍, 冷 静, 等). Chinese Journal of Spectroscopy Laboratory(光谱实验室)，2007, 24(3): 388.
[5] ZHU Ju-hui, LIU Tong-bin, ZHAO Jin-zhou, et al(朱炬辉，刘同斌，赵金洲，等). Natural Gas Exploration and Development(天然气勘探与开发), 2007, 30(4): 37，85.
[6] SHENG Hu, YANG Jing-shu(盛 琥, 杨景曙). Computer Engineering and Applications(计算机工程与应用)，2008，44(19)：213.
[7] Kassa L D. South African Journal of Plant and Soil, 2008, 25 (3): 135.
[8] Huang D, Guan P, Guo J, et al. BMC Infectious Diseases, 2008, 8：130.
[9] LIU Guang-li(刘广丽). Finance & Economy(金融经济), 2007(16): 107.
[10] CHEN Bin, CHEN Dan(陈 斌，陈 蛋). Spectronic Instruments and Analysis(光谱仪器与分析), 2005(4): 26.
[11] Zhou X, Xiang B, Wan Z, et al. Analytical Letters, 2007, 40(18): 3383.
[12] LIU Hui-jun, LIN Min, SHI Yang, et al(刘辉军, 林 敏, 施 秧, 等). Physical Testing and Chemical Analysis Part B (Chemical Analysis)(理化检验-化学分册), 2008, 44(3): 249.
[13] ZHANG Qiao-jie, WANG Yi-ming, WU Jing-zhu(张巧杰，王一鸣，吴静珠). Journal of China Agricultural University(中国农业大学学报), 2006, 11(2): 74.
[14] CHEN Xiao-jing, WU Di, YU Jia-jia, et al(陈孝敬，吴 迪，虞佳佳，等). Acta Optica Sinica(光学学报), 2008, 28(11): 2153.
[15] FANG Kai-tai, QUAN Hui, CHEN Qing-yun(方开泰，全 辉，陈庆云). Practical Regression Analysis(实用回归分析). Beijing: Science Press(北京：科学出版社), 1988. 274.
[16] HE Xiu-li, WANG Hao-hua(何秀丽, 王浩华). Journal of Gansu Lianhe University·Natural Science Edition(甘肃联合大学学报·自然科学版), 2008, 22(6): 1. Study on the Application of Ridge Regression to Near-Infrared
|
| [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] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
| [3] |
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. |
| [4] |
LI Wei1, TAN Feng2*, ZHANG Wei1, GAO Lu-si3, LI Jin-shan4. Application of Improved Random Frog Algorithm in Fast Identification of Soybean Varieties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3763-3769. |
| [5] |
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. |
| [6] |
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. |
| [7] |
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. |
| [8] |
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. |
| [9] |
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. |
| [10] |
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. |
| [11] |
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. |
| [12] |
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. |
| [13] |
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. |
| [14] |
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. |
| [15] |
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. |
|
|
|
|
