Research on Chemical Oxygen Demand Optical Detection Method Based on the Combination of Multi-Source Spectral Characteristics
WU Guo-qing, BI Wei-hong*
School of Information Science and Engineering, Yanshan University, and Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Qinhuangdao 066004, China
Abstract：A novel method based on multi-source spectral characteristics of the combination is proposed for chemical oxygen demand detection. First, the ultraviolet and near infrared spectrum of the actual water samples are collected respectively. After pretreatment of the spectrum data, the features of the spectrum are extracted by the nonnegative matrix factorization algorithm for training after normalization. Particle swarm and least squares support vector machines algorithm are applied to predicting chemical oxygen demand of the validation set of water samples. The effect of spectrum’s base number on the predicted results is discussed. The experimental results show that the best base number of the ultraviolet spectrum is 5, the best base number of the near infrared spectrum is 2; The validation set correlation coefficient of the prediction model is 0.999 8, and the root mean square error of prediction is 3.26 mg·L-1. Experimental results demonstrate that the nonnegative matrix factorization algorithm is more suitable for feature extraction of spectral data, and the least squares support vector machines algorithm as a quantitative model correction method of the actual water samples can get good prediction accuracy with different feature extraction methods(principal component analysis, independent component analysis), spectroscopic methods(ultraviolet spectrum method, near infrared spectrum method) and different combination pattern (data direct combination, combining data first, then feature extraction) respectively.
吴国庆，毕卫红* . 多源光谱特征组合的COD光学检测方法研究 [J]. 光谱学与光谱分析, 2014, 34(11): 3071-3074.
WU Guo-qing, BI Wei-hong* . Research on Chemical Oxygen Demand Optical Detection Method Based on the Combination of Multi-Source Spectral Characteristics . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(11): 3071-3074.
[1] Best D G, de Casseres K E. Water Pollution Control, 1978, 77: 138. [2] Dedkov Y M, Elizarova O V, Kel’ina S Y. Anal. Chem., 2000, 55: 777. [3] ZENG Tian-ling, WEN Zhi-yu, WEN Zhong-quan, et al(曾甜玲, 温志渝, 温中泉, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(4): 1098. [4] HE Jin-cheng, YANG Xiang-long, WANG Li-ren, et al(何金成, 杨祥龙, 王立人, 等). Journal of Zhejiang University(Engineering Science)(浙江大学学报·工学版), 2007, 41(5): 752. [5] Vasel J L, Praet E. Water Science and Technology, 2002, 45(4-5): 109. [6] Paatero P, Tapper U. Environmetrics, 1994, 5(2):111. [7] Farial S, Berry M W, Paul P V, et al. Information Processing and Management, 2006, 42(2): 373. [8] Carmona Saez P, Pascual Marqui R D, Tirado F, et al. BMC Bioinformatics, 2006, 78(7): 1212. [9] LI Jian-hong, JIANG Tong-min, HE Yu-zhu, et al(李建宏, 姜同敏, 何玉珠, 等). Journal of Beijing University of Aeronautics and Astronautics(北京航空航天大学学报), 2012, 38(12): 1639. [10] SUN Xu-dong, DONG Xiao-ling(孙旭东, 董小玲). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(14): 262. [11] GUO Wen-chuan, WANG Ming-hai, YUE Rong(郭文川, 王铭海, 岳 绒). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2013, 44(2): 142.
