光谱学与光谱分析 |
|
|
|
|
|
An Optimized Ultraviolet-Visible Spectrum Dual Optical Path Length Fusion Algorithm for Water Quality Monitoring |
WU De-cao1, 2, WEI Biao1*, XIONG Shuang-fei1, FENG Peng1, TANG Ge1, TANG Yuan1, LIU Juan1, CHEN Wei3, QIU Yu2, CHEN Yuan-yuan2, YE Xin4 |
1. Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China 2. Chongqing Industry Polytechnic College, Chongqing 401120, China 3. Municipal Gardens Bureau of Jiulongpo District of Chongqing, Chongqing 400050, China 4. Chongqing University of Technology, Chongqing 400054, China |
|
|
Abstract In terms of water quality monitoring based on the ultraviolet-visible spectroscopy, different optical path lengths of spectrometer probe need to be set to maintain higher signal-to-noise ratio of the spectra when the water body measured is complex and changeable. However, large numbers of experiments always have to be undertaken to get the appropriate optical path length, which is difficult to meet the demand of real-time, accurate, sensitive and stable online monitoring system. In this paper, an optimized spectra fusion algorithm was developed to improve the signal-to-noise ratio of fused spectra from two independent spectra that were acquired using two different optical path lengths. In the fusion algorithm, the sliding-pane method was applied to obtain the distribution of noise variance of the spectra, so the region of strong noise in the spectra could be determined. Due to different signal intensity of spectra with long and short optical path length, genetic algorithm was used to calculate the optimal gain matching rate of fusion. Finally, according to the distribution of noise variance, piecewise weighted method is applied to achieve a fusion spectrum with higher signal-to-noise ratio. The experimental results showed that the fusion algorithm could effectively enhance the signal-to-noise ratio of the fused spectra for each sample without altering the optical path length; the noise within 200~250 nm was suppressed and the low-noise and high-sensitivity spectra in the visible band were preserved; Zero interference was moved to the left of 220 nm of the spectrum. This means the fusion algorithm not only shows improvements in both signal-to-noise ratio and the detailed characteristics of the spectrum, but also reduces the excessive number of experiments in order to optimize optical path length and minimize noise in spectra. It has important practical significance to broaden the application range of the ultraviolet-visible spectroscopy based water quality monitoring system.
|
Received: 2016-04-04
Accepted: 2016-08-17
|
|
Corresponding Authors:
WEI Biao
E-mail: weibiao@cqu.edu.cn
|
|
[1] Thomas O, Théraulaz F, Cerdà V, et al. Trac Trends in Analytical Chemistry, 1997, 16(7): 419. [2] MU Xiu-sheng(穆秀圣). Research and Realization of Full Spectrum Online Water Quality Measuring Instrument(UV全光谱法在线水质测量仪的技术研究与实现). University of Electronic Science and Technology of China(电子科技大学), 2009. [3] Ruban G, Ruperd Y, Laveau B, et al. Water Science & Technology, A Journal of the International Association on Water Pollution Research, 2001, 44(2-3): 269. [4] SHEN Shuang, TANG Zhen-an, LI Tong(申 爽, 唐祯安, 李 彤). Chinese Journal of Scientific Instrument(仪器仪表学报), 2008, 29 (5): 1069. [5] Lienert B, Porter J, Sharma S K. Applied Optics, 2009, 48(24): 4762. [6] HE Jin-cheng, YANG Xiang-long, WANG Li-ren(何金成,杨祥龙,王立人). Journal of Infrared & Millimeter Waves(红外与毫米波学报), 2007, 26(4): 317. [7] Tang B. Research of Key Technologies on Water Quality Multi-Parameter Measurement System Based on UV-Vis Spectroscopy(紫外-可见光谱水质检测多参数测量系统的关键技术研究). Chongqing University(重庆大学), 2014. [8] Ocean Optics. USB2000+ Datasheet. Ocean Optics, 2015. [9] Thomas O, Cerdà V, Christopher Burgess. UV-visible Spectrophotometry of Water and Wastewater. Elsevier Science, 2007. [10] TANG Bin, WEI Biao, MAO Beng-jiang, et al(汤 斌, 魏 彪, 毛本将, 等). Laser & Optoelectronics Progress(激光与光电子学进展), 2014, 4: 197. [11] Donoho D L, Johnstone I M. Biometrika, 1994, 14(6): 425. [12] ZONG Jing-guo, QIN Han-lin, HE Guo-jing, et al(宗靖国, 秦翰林, 何国经,等). High Power Laser and Particle Beams(强激光与粒子束), 2013, 25(5). [13] Holland J H. Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975. [14] Goldberg D E. Computer-Aided Gas Pipeline Operation Using Genetic Algorithms and Rule Learning: (dissertation). University of Michigan, 1983. [15] Goldberg D E. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989. [16] Abramovich F, Sapatinas T, Silverman Y B. Journal of the Royal Statistical Society, Ser, B, 1998, 60: 725. |
[1] |
GAO Wei-ling, ZHANG Kai-hua*, XU Yan-fen, LIU Yu-fang*. Data Processing Method for Multi-Spectral Radiometric Thermometry Based on the Improved HPSOGA[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3659-3665. |
[2] |
WANG Yi-ru1, GAO Yang2, 3, WU Yong-gang4*, WANG Bo5*. Study of the Electronic Structure, Spectrum, and Excitation Properties of Sudan Red Ⅲ Molecule Based on the Density Functional Theory[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2426-2436. |
[3] |
NIU Fang-peng1, 2, LI Xin-guo1, 3*, BAI Yun-gang2, ZHAO Hui4. Hyperspectral Estimation Model of Soil Organic Carbon Content Based on Genetic Algorithm Fused With Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2232-2237. |
[4] |
LIU Mei-jun, TIAN Ning*, YU Ji*. Spectral Study on Mouse Oocyte Quality[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1376-1380. |
[5] |
CI Cheng-gang*, ZANG Jie-chao, LI Ming-fei*. DFT Study on Spectra of Mn-Carbonyl Molecular Complexes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1434-1441. |
[6] |
CHEN Qing1, TANG Bin1, 2*, LONG Zou-rong1, 2, MIAO Jun-feng1, HUANG Zi-heng1, DAI Ruo-chen1, SHI Sheng-hui1, ZHAO Ming-fu1, ZHONG Nian-bing1. Water Quality Classification Using Convolution Neural Network Based on UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 731-736. |
[7] |
WANG Ren-jie1, 2, FENG Peng1*, YANG Xing3, AN Le3, HUANG Pan1, LUO Yan1, HE Peng1, TANG Bin1, 2*. A Denoising Algorithm for Ultraviolet-Visible Spectrum Based on
CEEMDAN and Dual-Tree Complex Wavelet Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 976-983. |
[8] |
XU Meng-lei1, 2, GAO Yu3, ZHU Lin1, HAN Xiao-xia1, ZHAO Bing1*. Improved Sensitivity of Localized Surface Plasmon Resonance Using Silver Nanoparticles for Indirect Glyphosate Detection Based on Ninhydrin Reaction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 320-323. |
[9] |
ZHENG Kai-yi, SHEN Ye, ZHANG Wen, ZHOU Chen-guang, DING Fu-yuan, ZHANG Yang, ZHANG Rou-jia, SHI Ji-yong, ZOU Xiao-bo*. Interval Genetic Algorithm for Double Spectra and Its Applications in Calibration Transfer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3783-3788. |
[10] |
LI Qing-bo1, BI Zhi-qi1, CUI Hou-xin2, LANG Jia-ye2, SHEN Zhong-kai2. Detection of Total Organic Carbon in Surface Water Based on UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3423-3427. |
[11] |
LIU Meng-xuan1, 2, 3, 4, WU Qiong5, WANG Xu-quan1, 2, 4, CHEN Qi5, ZHANG Yong-gang1, 2, HUANG Song-lei1, 2*, FANG Jia-xiong1, 2*. Validity and Redundancy of Spectral Data in the Detection Algorithm of Sucrose-Doped Content in Tea[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3647-3652. |
[12] |
GUO Yang1, GUO Jun-xian1*, SHI Yong1, LI Xue-lian1, HUANG Hua2, LIU Yan-cen1. Estimation of Leaf Moisture Content in Cantaloupe Canopy Based on
SiPLS-CARS and GA-ELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2565-2571. |
[13] |
HUANG Qing1, XUE He-ru1*, LIU Jiang-ping1*, LIU Mei-chen1, HU Peng-wei1, SUN De-gang2. Spectral Selection Method Based on Ant Colony-Genetic Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2262-2268. |
[14] |
LUO Heng, Andy Hsitien Shen*. Based on Color Calculation and In-Situ Element Analyze to Study the Color Origin of Purple Chalcedony[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1891-1898. |
[15] |
JIANG Qing-hu1, LIU Feng1, YU Dong-yue2, 3, LUO Hui2, 3, LIANG Qiong3*, ZHANG Yan-jun3*. Rapid Measurement of the Pharmacological Active Constituents in Herba Epimedii Using Hyperspectral Analysis Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1445-1450. |
|
|
|
|