光谱学与光谱分析 |
|
|
|
|
|
Adaptive “3R” De-Noising Algorithm Based on Near Infrared Bi-Spectrum |
ZHAO Xiao-yu1,2, FANG Yi-ming1, TAN Feng2, WANG Zhi-gang3, TONG Liang4 |
1. Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China 2. College of Information Technology, Heilongjiang Bayi Agricultural University, Daqing 163319, China 3. College of Life Science and Forestry, Qiqihar University, Qiqihar 161006, China 4. Communication and Electronic Engineering Institute, Qiqihar University, Qiqihar 161006, China |
|
|
Abstract Adaptive de-noising algorithm is proposed based on transmission spectrum and absorption spectrum of near infrared. Near infrared transmission spectrum and absorption spectrum collected synchronously are decomposed into intrinsic mode functions by ensemble empirical mode decomposition; the intrinsic mode function is a single frequency component. Correlations between intrinsic mode functions and transmission spectrum, absorption spectrum were calculated, and the correlation between intrinsic mode functions of transmission spectrum and absorption spectrum was also computed. The results show that the intrinsic mode function with minimum correlation coefficient should be noise component. The self-correlation of this intrinsic mode function was analyzed to judge whether the intrinsic mode function is noise. IF the self-correlation is very large at the midpoint and is zero or very small at the other point of the spectrum, then the intrinsic mode function is noise component for judgment, based on which “3R” algorithm is named to judge whether the intrinsic mode function is noise component. Removing noise component, constructing spectral signal and circulating the previous decomposition was conducted, and the noise reduction process was ended until it did not meet the “3R” rule. To do experiment on the simulated spectrum with noise, the effect of de-noising with “3R” algorithm is better than EMD and EEMD low pass filter, and it is not so good as wavelet decomposition. In the real spectrum testing, the model was established between spectra treated by above methods with chlorophyll on three layers. BP neural network, and the model de-noised by “3R” method has the biggest correlation coefficient and prediction coefficient, but the smallest correction standard error and prediction standard error. “3R” method’s effects on the peak position and peak intensity of spectrum are the smallest among the four kinds of de-noising methods. Experiments show that the “3R” algorithm based on bi-spectrum can be used for near infrared spectra de-nosing without presetting the number of iterations, there is no need to consider layers of decomposition, also no need of basis function, and the adaptability is very strong.
|
Received: 2014-04-23
Accepted: 2014-08-18
|
|
Corresponding Authors:
ZHAO Xiao-yu
E-mail: xy_zhao77@163.com
|
|
[1] XU Bin, YANG Tao, TAN Bao-hua(徐 斌, 杨 涛, 谭保华). Nuclear Electronics & Detection Technology(核电子学与探测技术), 2011, 31(6): 702. [2] ZHAO Jin-hui, YUAN Hai-chao, LIU Mu-hua(赵进辉, 袁海超, 刘木华). Chinese Journal of Analytical Chemistry(分析化学), 2013, 41(4): 546. [3] ZHU Jin, SUN Dong-mei, CHEN Ling(朱 靳, 孙冬梅, 陈 玲). Acta Optica Sinica(光学学报), 2013, 33(2): 278. [4] ZHANG Yuan-yuan, LI Shun-ming, HU Yi-xian(张袁元, 李舜酩, 胡伊贤). Journal of Vibration and Shock(振动与冲击), 2013, 32(20): 61. [5] CHEN Zhong, FU He-chao(陈 忠, 符和超). Journal of Vibration and Shock(振动与冲击), 2013, 32(20): 124. [6] ZHAO Xiao-yu, FANG Yi-ming, WANG Zhi-gang, et al(赵肖宇, 方一鸣, 王志刚,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(12): 3255. [7] Huang N E,Shen Z,Long S R,et al. Proc. R Soc. Lond A,1998, 454: 903. [8] XIONG Xing-long, LI Meng, JIANG Li-hui(熊兴隆, 李 猛, 蒋立辉). Infrared and Laser Engineering(红外与激光工程), 2013, 42(6): 1628. [9] WANG Yuan-sheng, REN Xing-min, DENG Wang-qun(王元生, 任兴民, 邓旺群). Journal of Northwestern Polytechnical University(西北工业大学学报), 2013, 31(2): 272. [10] Wu Zhaohua,Huang N E. Advances in Adaptive Data Analysis,2009, 1(1): 1. [11] ZHANG De-xiang, WANG Ping, WU Xiao-pei(张德祥, 汪 萍, 吴小培). Journal of Vibration and Shock(振动与冲击), 2010,29(7): 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] |
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. |
[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] |
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. |
[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. |
|
|
|
|